RL4J: Add TransformProcess, part 2 (#8766)
* Part 2 of TransformProcess Signed-off-by: Alexandre Boulanger <aboulang2002@yahoo.com> * Fix compile errors Signed-off-by: Samuel Audet <samuel.audet@gmail.com> * Revert unrelated changes Signed-off-by: Samuel Audet <samuel.audet@gmail.com> Co-authored-by: Samuel Audet <samuel.audet@gmail.com>
This commit is contained in:
Alexandre Boulanger
GitHub
parent
0faf83b1b6
commit
8b10f0b876
@ -1,5 +1,5 @@
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/*******************************************************************************
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* Copyright (c) 2015-2019 Skymind, Inc.
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* Copyright (c) 2020 Konduit K.K.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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@ -17,57 +17,43 @@
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package org.deeplearning4j.rl4j.observation;
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import lombok.Getter;
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import lombok.Setter;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.dataset.api.DataSet;
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import org.nd4j.linalg.factory.Nd4j;
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/**
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* Presently only a dummy container. Will contain observation channels when done.
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* Represent an observation from the environment
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*
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* @author Alexandre Boulanger
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*/
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public class Observation {
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// TODO: Presently only a dummy container. Will contain observation channels when done.
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private final DataSet data;
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/**
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* A singleton representing a skipped observation
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*/
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public static Observation SkippedObservation = new Observation(null);
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@Getter @Setter
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private boolean skipped;
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/**
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* @return A INDArray containing the data of the observation
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*/
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@Getter
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private final INDArray data;
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public Observation(INDArray[] data) {
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this(data, false);
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public boolean isSkipped() {
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return data == null;
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}
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public Observation(INDArray[] data, boolean shouldReshape) {
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INDArray features = Nd4j.concat(0, data);
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if(shouldReshape) {
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features = reshape(features);
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}
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this.data = new org.nd4j.linalg.dataset.DataSet(features, null);
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}
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// FIXME: Remove -- only used in unit tests
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public Observation(INDArray data) {
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this.data = new org.nd4j.linalg.dataset.DataSet(data, null);
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}
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private INDArray reshape(INDArray source) {
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long[] shape = source.shape();
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long[] nshape = new long[shape.length + 1];
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nshape[0] = 1;
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System.arraycopy(shape, 0, nshape, 1, shape.length);
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return source.reshape(nshape);
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}
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private Observation(DataSet data) {
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this.data = data;
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}
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/**
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* Creates a duplicate instance of the current observation
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* @return
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*/
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public Observation dup() {
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return new Observation(new org.nd4j.linalg.dataset.DataSet(data.getFeatures().dup(), null));
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}
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if(data == null) {
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return SkippedObservation;
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}
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public INDArray getData() {
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return data.getFeatures();
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return new Observation(data.dup());
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}
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}
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@ -0,0 +1,231 @@
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/*******************************************************************************
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* Copyright (c) 2020 Konduit K.K.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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package org.deeplearning4j.rl4j.observation.transform;
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import org.apache.commons.lang3.NotImplementedException;
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import org.deeplearning4j.rl4j.helper.INDArrayHelper;
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import org.deeplearning4j.rl4j.observation.Observation;
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import org.nd4j.base.Preconditions;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.dataset.api.DataSet;
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import org.nd4j.linalg.dataset.api.DataSetPreProcessor;
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import org.nd4j.shade.guava.collect.Maps;
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import org.datavec.api.transform.Operation;
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import java.util.ArrayList;
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import java.util.HashSet;
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import java.util.List;
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import java.util.Map;
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/**
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* A TransformProcess will build an {@link Observation Observation} from the raw data coming from the environment.
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* Three types of steps are available:
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* 1) A {@link FilterOperation FilterOperation}: Used to determine if an observation should be skipped.
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* 2) An {@link Operation Operation}: Applies a transform and/or conversion to an observation channel.
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* 3) A {@link DataSetPreProcessor DataSetPreProcessor}: Applies a DataSetPreProcessor to the observation channel. The data in the channel must be a DataSet.
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*
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* Instances of the three types above can be called in any order. The only requirement is that when build() is called,
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* all channels must be instances of INDArrays or DataSets
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*
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* NOTE: Presently, only single-channels observations are supported.
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*
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* @author Alexandre Boulanger
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*/
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public class TransformProcess {
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private final List<Map.Entry<String, Object>> operations;
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private final String[] channelNames;
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private final HashSet<String> operationsChannelNames;
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private TransformProcess(Builder builder, String... channelNames) {
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operations = builder.operations;
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this.channelNames = channelNames;
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this.operationsChannelNames = builder.requiredChannelNames;
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}
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/**
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* This method will call reset() of all steps implementing {@link ResettableOperation ResettableOperation} in the transform process.
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*/
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public void reset() {
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for(Map.Entry<String, Object> entry : operations) {
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if(entry.getValue() instanceof ResettableOperation) {
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((ResettableOperation) entry.getValue()).reset();
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}
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}
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}
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/**
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* Transforms the channel data into an Observation or a skipped observation depending on the specific steps in the transform process.
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*
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* @param channelsData A Map that maps the channel name to its data.
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* @param currentObservationStep The observation's step number within the episode.
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* @param isFinalObservation True if the observation is the last of the episode.
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* @return An observation (may be a skipped observation)
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*/
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public Observation transform(Map<String, Object> channelsData, int currentObservationStep, boolean isFinalObservation) {
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// null or empty channelData
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Preconditions.checkArgument(channelsData != null && channelsData.size() != 0, "Error: channelsData not supplied.");
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// Check that all channels have data
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for(Map.Entry<String, Object> channel : channelsData.entrySet()) {
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Preconditions.checkNotNull(channel.getValue(), "Error: data of channel '%s' is null", channel.getKey());
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}
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// Check that all required channels are present
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for(String channelName : operationsChannelNames) {
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Preconditions.checkArgument(channelsData.containsKey(channelName), "The channelsData map does not contain the channel '%s'", channelName);
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}
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for(Map.Entry<String, Object> entry : operations) {
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// Filter
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if(entry.getValue() instanceof FilterOperation) {
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FilterOperation filterOperation = (FilterOperation)entry.getValue();
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if(filterOperation.isSkipped(channelsData, currentObservationStep, isFinalObservation)) {
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return Observation.SkippedObservation;
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}
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}
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// Transform
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// null results are considered skipped observations
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else if(entry.getValue() instanceof Operation) {
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Operation transformOperation = (Operation)entry.getValue();
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Object transformed = transformOperation.transform(channelsData.get(entry.getKey()));
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if(transformed == null) {
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return Observation.SkippedObservation;
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}
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channelsData.replace(entry.getKey(), transformed);
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}
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// PreProcess
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else if(entry.getValue() instanceof DataSetPreProcessor) {
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Object channelData = channelsData.get(entry.getKey());
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DataSetPreProcessor dataSetPreProcessor = (DataSetPreProcessor)entry.getValue();
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if(!(channelData instanceof DataSet)) {
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throw new IllegalArgumentException("The channel data must be a DataSet to call preProcess");
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}
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dataSetPreProcessor.preProcess((DataSet)channelData);
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}
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else {
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throw new IllegalArgumentException(String.format("Unknown operation: '%s'", entry.getValue().getClass().getName()));
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}
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}
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// Check that all channels used to build the observation are instances of
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// INDArray or DataSet
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// TODO: Add support for an interface with a toINDArray() method
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for(String channelName : channelNames) {
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Object channelData = channelsData.get(channelName);
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INDArray finalChannelData;
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if(channelData instanceof DataSet) {
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finalChannelData = ((DataSet)channelData).getFeatures();
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}
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else if(channelData instanceof INDArray) {
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finalChannelData = (INDArray) channelData;
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}
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else {
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throw new IllegalStateException("All channels used to build the observation must be instances of DataSet or INDArray");
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}
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// The dimension 0 of all INDArrays must be 1 (batch count)
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channelsData.replace(channelName, INDArrayHelper.forceCorrectShape(finalChannelData));
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}
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// TODO: Add support to multi-channel observations
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INDArray data = ((INDArray) channelsData.get(channelNames[0]));
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return new Observation(data);
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}
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/**
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* @return An instance of a builder
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*/
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public static Builder builder() {
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return new Builder();
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}
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public static class Builder {
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private final List<Map.Entry<String, Object>> operations = new ArrayList<Map.Entry<String, Object>>();
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private final HashSet<String> requiredChannelNames = new HashSet<String>();
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/**
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* Add a filter to the transform process steps. Used to skip observations on certain conditions.
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* See {@link FilterOperation FilterOperation}
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* @param filterOperation An instance
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*/
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public Builder filter(FilterOperation filterOperation) {
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Preconditions.checkNotNull(filterOperation, "The filterOperation must not be null");
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operations.add((Map.Entry)Maps.immutableEntry(null, filterOperation));
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return this;
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}
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/**
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* Add a transform to the steps. The transform can change the data and / or change the type of the data
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* (e.g. Byte[] to a ImageWritable)
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*
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* @param targetChannel The name of the channel to which the transform is applied.
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* @param transformOperation An instance of {@link Operation Operation}
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*/
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public Builder transform(String targetChannel, Operation transformOperation) {
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Preconditions.checkNotNull(targetChannel, "The targetChannel must not be null");
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Preconditions.checkNotNull(transformOperation, "The transformOperation must not be null");
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requiredChannelNames.add(targetChannel);
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operations.add((Map.Entry)Maps.immutableEntry(targetChannel, transformOperation));
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return this;
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}
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/**
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* Add a DataSetPreProcessor to the steps. The channel must be a DataSet instance at this step.
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* @param targetChannel The name of the channel to which the pre processor is applied.
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* @param dataSetPreProcessor
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*/
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public Builder preProcess(String targetChannel, DataSetPreProcessor dataSetPreProcessor) {
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Preconditions.checkNotNull(targetChannel, "The targetChannel must not be null");
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Preconditions.checkNotNull(dataSetPreProcessor, "The dataSetPreProcessor must not be null");
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requiredChannelNames.add(targetChannel);
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operations.add((Map.Entry)Maps.immutableEntry(targetChannel, dataSetPreProcessor));
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return this;
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}
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/**
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* Builds the TransformProcess.
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* @param channelNames A subset of channel names to be used to build the observation
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* @return An instance of TransformProcess
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*/
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public TransformProcess build(String... channelNames) {
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if(channelNames.length == 0) {
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throw new IllegalArgumentException("At least one channel must be supplied.");
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}
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for(String channelName : channelNames) {
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Preconditions.checkNotNull(channelName, "Error: got a null channel name");
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requiredChannelNames.add(channelName);
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}
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// TODO: Remove when multi-channel observation is supported
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if(channelNames.length != 1) {
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throw new NotImplementedException("Multi-channel observations is not presently supported.");
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}
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return new TransformProcess(this, channelNames);
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}
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}
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}
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@ -25,14 +25,14 @@ import org.nd4j.linalg.factory.Nd4j;
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import static org.bytedeco.opencv.global.opencv_core.CV_32FC;
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public class EncodableToImageWriteableTransform implements Operation<Encodable, ImageWritable> {
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public class EncodableToImageWritableTransform implements Operation<Encodable, ImageWritable> {
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private final OpenCVFrameConverter.ToMat converter = new OpenCVFrameConverter.ToMat();
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private final int height;
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private final int width;
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private final int colorChannels;
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public EncodableToImageWriteableTransform(int height, int width, int colorChannels) {
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public EncodableToImageWritableTransform(int height, int width, int colorChannels) {
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this.height = height;
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this.width = width;
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this.colorChannels = colorChannels;
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@ -1,3 +1,18 @@
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/*******************************************************************************
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* Copyright (c) 2020 Konduit K.K.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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package org.deeplearning4j.rl4j.observation.transform.legacy;
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import org.datavec.api.transform.Operation;
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@ -10,13 +25,13 @@ import org.nd4j.linalg.factory.Nd4j;
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import java.io.IOException;
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public class ImageWriteableToINDArrayTransform implements Operation<ImageWritable, INDArray> {
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public class ImageWritableToINDArrayTransform implements Operation<ImageWritable, INDArray> {
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private final int height;
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private final int width;
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private final NativeImageLoader loader;
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public ImageWriteableToINDArrayTransform(int height, int width) {
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public ImageWritableToINDArrayTransform(int height, int width) {
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this.height = height;
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this.width = width;
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this.loader = new NativeImageLoader(height, width);
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@ -0,0 +1,44 @@
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/*******************************************************************************
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* Copyright (c) 2020 Konduit K.K.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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package org.deeplearning4j.rl4j.observation.transform.operation;
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import org.datavec.api.transform.Operation;
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import org.nd4j.base.Preconditions;
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import org.nd4j.linalg.api.ndarray.INDArray;
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public class SimpleNormalizationTransform implements Operation<INDArray, INDArray> {
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private final double offset;
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private final double divisor;
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public SimpleNormalizationTransform(double min, double max) {
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Preconditions.checkArgument(min < max, "Min must be smaller than max.");
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this.offset = min;
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this.divisor = (max - min);
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}
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@Override
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public INDArray transform(INDArray input) {
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if(offset != 0.0) {
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input.subi(offset);
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}
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input.divi(divisor);
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return input;
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}
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}
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@ -3,37 +3,95 @@ package org.deeplearning4j.rl4j.util;
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import lombok.AccessLevel;
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import lombok.Getter;
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import lombok.Setter;
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import org.datavec.image.transform.ColorConversionTransform;
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import org.datavec.image.transform.CropImageTransform;
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import org.datavec.image.transform.MultiImageTransform;
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import org.datavec.image.transform.ResizeImageTransform;
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import org.deeplearning4j.gym.StepReply;
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import org.deeplearning4j.rl4j.learning.EpochStepCounter;
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import org.deeplearning4j.rl4j.learning.IHistoryProcessor;
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import org.deeplearning4j.rl4j.mdp.MDP;
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import org.deeplearning4j.rl4j.observation.Observation;
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import org.deeplearning4j.rl4j.observation.transform.TransformProcess;
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import org.deeplearning4j.rl4j.observation.transform.filter.UniformSkippingFilter;
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import org.deeplearning4j.rl4j.observation.transform.legacy.EncodableToINDArrayTransform;
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import org.deeplearning4j.rl4j.observation.transform.legacy.EncodableToImageWritableTransform;
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import org.deeplearning4j.rl4j.observation.transform.legacy.ImageWritableToINDArrayTransform;
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import org.deeplearning4j.rl4j.observation.transform.operation.HistoryMergeTransform;
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import org.deeplearning4j.rl4j.observation.transform.operation.SimpleNormalizationTransform;
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import org.deeplearning4j.rl4j.space.ActionSpace;
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import org.deeplearning4j.rl4j.space.Encodable;
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import org.deeplearning4j.rl4j.space.ObservationSpace;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.factory.Nd4j;
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import java.util.HashMap;
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import java.util.Map;
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import static org.bytedeco.opencv.global.opencv_imgproc.COLOR_BGR2GRAY;
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public class LegacyMDPWrapper<O, A, AS extends ActionSpace<A>> implements MDP<Observation, A, AS> {
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@Getter
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private final MDP<O, A, AS> wrappedMDP;
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@Getter
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private final WrapperObservationSpace observationSpace;
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private final int[] shape;
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||||
|
||||
@Getter(AccessLevel.PRIVATE) @Setter(AccessLevel.PUBLIC)
|
||||
@Setter
|
||||
private TransformProcess transformProcess;
|
||||
|
||||
@Getter(AccessLevel.PRIVATE)
|
||||
private IHistoryProcessor historyProcessor;
|
||||
|
||||
private final EpochStepCounter epochStepCounter;
|
||||
|
||||
private int skipFrame = 1;
|
||||
private int requiredFrame = 0;
|
||||
|
||||
public LegacyMDPWrapper(MDP<O, A, AS> wrappedMDP, IHistoryProcessor historyProcessor, EpochStepCounter epochStepCounter) {
|
||||
this.wrappedMDP = wrappedMDP;
|
||||
this.observationSpace = new WrapperObservationSpace(wrappedMDP.getObservationSpace().getShape());
|
||||
this.shape = wrappedMDP.getObservationSpace().getShape();
|
||||
this.observationSpace = new WrapperObservationSpace(shape);
|
||||
this.historyProcessor = historyProcessor;
|
||||
this.epochStepCounter = epochStepCounter;
|
||||
|
||||
setHistoryProcessor(historyProcessor);
|
||||
}
|
||||
|
||||
public void setHistoryProcessor(IHistoryProcessor historyProcessor) {
|
||||
this.historyProcessor = historyProcessor;
|
||||
createTransformProcess();
|
||||
}
|
||||
|
||||
private void createTransformProcess() {
|
||||
IHistoryProcessor historyProcessor = getHistoryProcessor();
|
||||
|
||||
if(historyProcessor != null && shape.length == 3) {
|
||||
int skipFrame = historyProcessor.getConf().getSkipFrame();
|
||||
|
||||
int finalHeight = historyProcessor.getConf().getCroppingHeight();
|
||||
int finalWidth = historyProcessor.getConf().getCroppingWidth();
|
||||
|
||||
transformProcess = TransformProcess.builder()
|
||||
.filter(new UniformSkippingFilter(skipFrame))
|
||||
.transform("data", new EncodableToImageWritableTransform(shape[0], shape[1], shape[2]))
|
||||
.transform("data", new MultiImageTransform(
|
||||
new ResizeImageTransform(historyProcessor.getConf().getRescaledWidth(), historyProcessor.getConf().getRescaledHeight()),
|
||||
new ColorConversionTransform(COLOR_BGR2GRAY),
|
||||
new CropImageTransform(historyProcessor.getConf().getOffsetY(), historyProcessor.getConf().getOffsetX(), finalHeight, finalWidth)
|
||||
))
|
||||
.transform("data", new ImageWritableToINDArrayTransform(finalHeight, finalWidth))
|
||||
.transform("data", new SimpleNormalizationTransform(0.0, 255.0))
|
||||
.transform("data", HistoryMergeTransform.builder()
|
||||
.isFirstDimenstionBatch(true)
|
||||
.build())
|
||||
.build("data");
|
||||
}
|
||||
else {
|
||||
transformProcess = TransformProcess.builder()
|
||||
.transform("data", new EncodableToINDArrayTransform(shape))
|
||||
.build("data");
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
@ -43,25 +101,17 @@ public class LegacyMDPWrapper<O, A, AS extends ActionSpace<A>> implements MDP<Ob
|
||||
|
||||
@Override
|
||||
public Observation reset() {
|
||||
INDArray rawObservation = getInput(wrappedMDP.reset());
|
||||
transformProcess.reset();
|
||||
|
||||
IHistoryProcessor historyProcessor = getHistoryProcessor();
|
||||
if(historyProcessor != null) {
|
||||
historyProcessor.record(rawObservation);
|
||||
}
|
||||
|
||||
Observation observation = new Observation(new INDArray[] { rawObservation }, false);
|
||||
O rawResetResponse = wrappedMDP.reset();
|
||||
record(rawResetResponse);
|
||||
|
||||
if(historyProcessor != null) {
|
||||
skipFrame = historyProcessor.getConf().getSkipFrame();
|
||||
requiredFrame = skipFrame * (historyProcessor.getConf().getHistoryLength() - 1);
|
||||
|
||||
historyProcessor.add(rawObservation);
|
||||
}
|
||||
|
||||
observation.setSkipped(skipFrame != 0);
|
||||
|
||||
return observation;
|
||||
Map<String, Object> channelsData = buildChannelsData(rawResetResponse);
|
||||
return transformProcess.transform(channelsData, 0, false);
|
||||
}
|
||||
|
||||
@Override
|
||||
@ -71,32 +121,32 @@ public class LegacyMDPWrapper<O, A, AS extends ActionSpace<A>> implements MDP<Ob
|
||||
StepReply<O> rawStepReply = wrappedMDP.step(a);
|
||||
INDArray rawObservation = getInput(rawStepReply.getObservation());
|
||||
|
||||
int stepOfObservation = epochStepCounter.getCurrentEpochStep() + 1;
|
||||
|
||||
if(historyProcessor != null) {
|
||||
historyProcessor.record(rawObservation);
|
||||
|
||||
if (stepOfObservation % skipFrame == 0) {
|
||||
historyProcessor.add(rawObservation);
|
||||
}
|
||||
}
|
||||
|
||||
Observation observation;
|
||||
if(historyProcessor != null && stepOfObservation >= requiredFrame) {
|
||||
observation = new Observation(historyProcessor.getHistory(), true);
|
||||
observation.getData().muli(1.0 / historyProcessor.getScale());
|
||||
}
|
||||
else {
|
||||
observation = new Observation(new INDArray[] { rawObservation }, false);
|
||||
}
|
||||
|
||||
if(stepOfObservation % skipFrame != 0 || stepOfObservation < requiredFrame) {
|
||||
observation.setSkipped(true);
|
||||
}
|
||||
int stepOfObservation = epochStepCounter.getCurrentEpochStep() + 1;
|
||||
|
||||
Map<String, Object> channelsData = buildChannelsData(rawStepReply.getObservation());
|
||||
Observation observation = transformProcess.transform(channelsData, stepOfObservation, rawStepReply.isDone());
|
||||
return new StepReply<Observation>(observation, rawStepReply.getReward(), rawStepReply.isDone(), rawStepReply.getInfo());
|
||||
}
|
||||
|
||||
private void record(O obs) {
|
||||
INDArray rawObservation = getInput(obs);
|
||||
|
||||
IHistoryProcessor historyProcessor = getHistoryProcessor();
|
||||
if(historyProcessor != null) {
|
||||
historyProcessor.record(rawObservation);
|
||||
}
|
||||
}
|
||||
|
||||
private Map<String, Object> buildChannelsData(final O obs) {
|
||||
return new HashMap<String, Object>() {{
|
||||
put("data", obs);
|
||||
}};
|
||||
}
|
||||
|
||||
@Override
|
||||
public void close() {
|
||||
wrappedMDP.close();
|
||||
|
||||
@ -34,6 +34,7 @@ public class AsyncThreadDiscreteTest {
|
||||
MockPolicy policyMock = new MockPolicy();
|
||||
MockAsyncConfiguration config = new MockAsyncConfiguration(5, 100, 0, 0, 2, 5,0, 0, 0, 0);
|
||||
TestAsyncThreadDiscrete sut = new TestAsyncThreadDiscrete(asyncGlobalMock, mdpMock, listeners, 0, 0, policyMock, config, hpMock);
|
||||
sut.getLegacyMDPWrapper().setTransformProcess(MockMDP.buildTransformProcess(observationSpace.getShape(), hpConf.getSkipFrame(), hpConf.getHistoryLength()));
|
||||
|
||||
// Act
|
||||
sut.run();
|
||||
@ -60,12 +61,6 @@ public class AsyncThreadDiscreteTest {
|
||||
assertEquals(2, asyncGlobalMock.enqueueCallCount);
|
||||
|
||||
// HistoryProcessor
|
||||
double[] expectedAddValues = new double[] { 0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0 };
|
||||
assertEquals(expectedAddValues.length, hpMock.addCalls.size());
|
||||
for(int i = 0; i < expectedAddValues.length; ++i) {
|
||||
assertEquals(expectedAddValues[i], hpMock.addCalls.get(i).getDouble(0), 0.00001);
|
||||
}
|
||||
|
||||
double[] expectedRecordValues = new double[] { 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, };
|
||||
assertEquals(expectedRecordValues.length, hpMock.recordCalls.size());
|
||||
for(int i = 0; i < expectedRecordValues.length; ++i) {
|
||||
|
||||
@ -138,6 +138,7 @@ public class AsyncThreadTest {
|
||||
asyncGlobal.setMaxLoops(numEpochs);
|
||||
listeners.add(listener);
|
||||
sut.setHistoryProcessor(historyProcessor);
|
||||
sut.getLegacyMDPWrapper().setTransformProcess(MockMDP.buildTransformProcess(observationSpace.getShape(), hpConf.getSkipFrame(), hpConf.getHistoryLength()));
|
||||
}
|
||||
}
|
||||
|
||||
@ -209,7 +210,4 @@ public class AsyncThreadTest {
|
||||
int nstep;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
}
|
||||
|
||||
@ -18,7 +18,7 @@ public class ExpReplayTest {
|
||||
ExpReplay<Integer> sut = new ExpReplay<Integer>(2, 1, randomMock);
|
||||
|
||||
// Act
|
||||
Transition<Integer> transition = buildTransition(new Observation(new INDArray[] { Nd4j.create(1) }),
|
||||
Transition<Integer> transition = buildTransition(buildObservation(),
|
||||
123, 234, new Observation(Nd4j.create(1)));
|
||||
sut.store(transition);
|
||||
List<Transition<Integer>> results = sut.getBatch(1);
|
||||
@ -36,11 +36,11 @@ public class ExpReplayTest {
|
||||
ExpReplay<Integer> sut = new ExpReplay<Integer>(2, 1, randomMock);
|
||||
|
||||
// Act
|
||||
Transition<Integer> transition1 = buildTransition(new Observation(new INDArray[] { Nd4j.create(1) }),
|
||||
Transition<Integer> transition1 = buildTransition(buildObservation(),
|
||||
1, 2, new Observation(Nd4j.create(1)));
|
||||
Transition<Integer> transition2 = buildTransition(new Observation(new INDArray[] { Nd4j.create(1) }),
|
||||
Transition<Integer> transition2 = buildTransition(buildObservation(),
|
||||
3, 4, new Observation(Nd4j.create(1)));
|
||||
Transition<Integer> transition3 = buildTransition(new Observation(new INDArray[] { Nd4j.create(1) }),
|
||||
Transition<Integer> transition3 = buildTransition(buildObservation(),
|
||||
5, 6, new Observation(Nd4j.create(1)));
|
||||
sut.store(transition1);
|
||||
sut.store(transition2);
|
||||
@ -78,11 +78,11 @@ public class ExpReplayTest {
|
||||
ExpReplay<Integer> sut = new ExpReplay<Integer>(5, 1, randomMock);
|
||||
|
||||
// Act
|
||||
Transition<Integer> transition1 = buildTransition(new Observation(new INDArray[] { Nd4j.create(1) }),
|
||||
Transition<Integer> transition1 = buildTransition(buildObservation(),
|
||||
1, 2, new Observation(Nd4j.create(1)));
|
||||
Transition<Integer> transition2 = buildTransition(new Observation(new INDArray[] { Nd4j.create(1) }),
|
||||
Transition<Integer> transition2 = buildTransition(buildObservation(),
|
||||
3, 4, new Observation(Nd4j.create(1)));
|
||||
Transition<Integer> transition3 = buildTransition(new Observation(new INDArray[] { Nd4j.create(1) }),
|
||||
Transition<Integer> transition3 = buildTransition(buildObservation(),
|
||||
5, 6, new Observation(Nd4j.create(1)));
|
||||
sut.store(transition1);
|
||||
sut.store(transition2);
|
||||
@ -100,11 +100,11 @@ public class ExpReplayTest {
|
||||
ExpReplay<Integer> sut = new ExpReplay<Integer>(5, 1, randomMock);
|
||||
|
||||
// Act
|
||||
Transition<Integer> transition1 = buildTransition(new Observation(new INDArray[] { Nd4j.create(1) }),
|
||||
Transition<Integer> transition1 = buildTransition(buildObservation(),
|
||||
1, 2, new Observation(Nd4j.create(1)));
|
||||
Transition<Integer> transition2 = buildTransition(new Observation(new INDArray[] { Nd4j.create(1) }),
|
||||
Transition<Integer> transition2 = buildTransition(buildObservation(),
|
||||
3, 4, new Observation(Nd4j.create(1)));
|
||||
Transition<Integer> transition3 = buildTransition(new Observation(new INDArray[] { Nd4j.create(1) }),
|
||||
Transition<Integer> transition3 = buildTransition(buildObservation(),
|
||||
5, 6, new Observation(Nd4j.create(1)));
|
||||
sut.store(transition1);
|
||||
sut.store(transition2);
|
||||
@ -131,15 +131,15 @@ public class ExpReplayTest {
|
||||
ExpReplay<Integer> sut = new ExpReplay<Integer>(5, 1, randomMock);
|
||||
|
||||
// Act
|
||||
Transition<Integer> transition1 = buildTransition(new Observation(new INDArray[] { Nd4j.create(1) }),
|
||||
Transition<Integer> transition1 = buildTransition(buildObservation(),
|
||||
1, 2, new Observation(Nd4j.create(1)));
|
||||
Transition<Integer> transition2 = buildTransition(new Observation(new INDArray[] { Nd4j.create(1) }),
|
||||
Transition<Integer> transition2 = buildTransition(buildObservation(),
|
||||
3, 4, new Observation(Nd4j.create(1)));
|
||||
Transition<Integer> transition3 = buildTransition(new Observation(new INDArray[] { Nd4j.create(1) }),
|
||||
Transition<Integer> transition3 = buildTransition(buildObservation(),
|
||||
5, 6, new Observation(Nd4j.create(1)));
|
||||
Transition<Integer> transition4 = buildTransition(new Observation(new INDArray[] { Nd4j.create(1) }),
|
||||
Transition<Integer> transition4 = buildTransition(buildObservation(),
|
||||
7, 8, new Observation(Nd4j.create(1)));
|
||||
Transition<Integer> transition5 = buildTransition(new Observation(new INDArray[] { Nd4j.create(1) }),
|
||||
Transition<Integer> transition5 = buildTransition(buildObservation(),
|
||||
9, 10, new Observation(Nd4j.create(1)));
|
||||
sut.store(transition1);
|
||||
sut.store(transition2);
|
||||
@ -168,15 +168,15 @@ public class ExpReplayTest {
|
||||
ExpReplay<Integer> sut = new ExpReplay<Integer>(5, 1, randomMock);
|
||||
|
||||
// Act
|
||||
Transition<Integer> transition1 = buildTransition(new Observation(new INDArray[] { Nd4j.create(1) }),
|
||||
Transition<Integer> transition1 = buildTransition(buildObservation(),
|
||||
1, 2, new Observation(Nd4j.create(1)));
|
||||
Transition<Integer> transition2 = buildTransition(new Observation(new INDArray[] { Nd4j.create(1) }),
|
||||
Transition<Integer> transition2 = buildTransition(buildObservation(),
|
||||
3, 4, new Observation(Nd4j.create(1)));
|
||||
Transition<Integer> transition3 = buildTransition(new Observation(new INDArray[] { Nd4j.create(1) }),
|
||||
Transition<Integer> transition3 = buildTransition(buildObservation(),
|
||||
5, 6, new Observation(Nd4j.create(1)));
|
||||
Transition<Integer> transition4 = buildTransition(new Observation(new INDArray[] { Nd4j.create(1) }),
|
||||
Transition<Integer> transition4 = buildTransition(buildObservation(),
|
||||
7, 8, new Observation(Nd4j.create(1)));
|
||||
Transition<Integer> transition5 = buildTransition(new Observation(new INDArray[] { Nd4j.create(1) }),
|
||||
Transition<Integer> transition5 = buildTransition(buildObservation(),
|
||||
9, 10, new Observation(Nd4j.create(1)));
|
||||
sut.store(transition1);
|
||||
sut.store(transition2);
|
||||
@ -204,4 +204,8 @@ public class ExpReplayTest {
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
private Observation buildObservation() {
|
||||
return new Observation(Nd4j.create(1, 1));
|
||||
}
|
||||
}
|
||||
|
||||
@ -193,11 +193,11 @@ public class TransitionTest {
|
||||
Nd4j.create(obs[1]).reshape(1, 3),
|
||||
Nd4j.create(obs[2]).reshape(1, 3),
|
||||
};
|
||||
return new Observation(history);
|
||||
return new Observation(Nd4j.concat(0, history));
|
||||
}
|
||||
|
||||
private Observation buildObservation(double[] obs) {
|
||||
return new Observation(new INDArray[] { Nd4j.create(obs).reshape(1, 3) });
|
||||
return new Observation(Nd4j.create(obs).reshape(1, 3));
|
||||
}
|
||||
|
||||
private Observation buildNextObservation(double[][] obs, double[] nextObs) {
|
||||
@ -206,7 +206,7 @@ public class TransitionTest {
|
||||
Nd4j.create(obs[0]).reshape(1, 3),
|
||||
Nd4j.create(obs[1]).reshape(1, 3),
|
||||
};
|
||||
return new Observation(nextHistory);
|
||||
return new Observation(Nd4j.concat(0, nextHistory));
|
||||
}
|
||||
|
||||
private Transition buildTransition(Observation observation, int action, double reward, Observation nextObservation) {
|
||||
|
||||
@ -50,6 +50,7 @@ public class QLearningDiscreteTest {
|
||||
IHistoryProcessor.Configuration hpConf = new IHistoryProcessor.Configuration(5, 4, 4, 4, 4, 0, 0, 2);
|
||||
MockHistoryProcessor hp = new MockHistoryProcessor(hpConf);
|
||||
sut.setHistoryProcessor(hp);
|
||||
sut.getLegacyMDPWrapper().setTransformProcess(MockMDP.buildTransformProcess(observationSpace.getShape(), hpConf.getSkipFrame(), hpConf.getHistoryLength()));
|
||||
List<QLearning.QLStepReturn<MockEncodable>> results = new ArrayList<>();
|
||||
|
||||
// Act
|
||||
@ -62,11 +63,7 @@ public class QLearningDiscreteTest {
|
||||
for(int i = 0; i < expectedRecords.length; ++i) {
|
||||
assertEquals(expectedRecords[i], hp.recordCalls.get(i).getDouble(0), 0.0001);
|
||||
}
|
||||
double[] expectedAdds = new double[] { 0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 22.0, 24.0 };
|
||||
assertEquals(expectedAdds.length, hp.addCalls.size());
|
||||
for(int i = 0; i < expectedAdds.length; ++i) {
|
||||
assertEquals(expectedAdds[i], hp.addCalls.get(i).getDouble(0), 0.0001);
|
||||
}
|
||||
|
||||
assertEquals(0, hp.startMonitorCallCount);
|
||||
assertEquals(0, hp.stopMonitorCallCount);
|
||||
|
||||
|
||||
@ -106,7 +106,7 @@ public class DoubleDQNTest {
|
||||
}
|
||||
|
||||
private Observation buildObservation(double[] data) {
|
||||
return new Observation(new INDArray[]{Nd4j.create(data).reshape(1, 2)});
|
||||
return new Observation(Nd4j.create(data).reshape(1, 2));
|
||||
}
|
||||
|
||||
private Transition<Integer> builtTransition(Observation observation, Integer action, double reward, boolean isTerminal, Observation nextObservation) {
|
||||
|
||||
@ -105,7 +105,7 @@ public class StandardDQNTest {
|
||||
}
|
||||
|
||||
private Observation buildObservation(double[] data) {
|
||||
return new Observation(new INDArray[]{Nd4j.create(data).reshape(1, 2)});
|
||||
return new Observation(Nd4j.create(data).reshape(1, 2));
|
||||
}
|
||||
|
||||
private Transition<Integer> buildTransition(Observation observation, Integer action, double reward, boolean isTerminal, Observation nextObservation) {
|
||||
|
||||
@ -0,0 +1,378 @@
|
||||
package org.deeplearning4j.rl4j.observation.transform;
|
||||
|
||||
import org.deeplearning4j.rl4j.observation.Observation;
|
||||
import org.junit.Test;
|
||||
import org.nd4j.linalg.dataset.api.DataSet;
|
||||
import org.nd4j.linalg.dataset.api.DataSetPreProcessor;
|
||||
import org.nd4j.linalg.factory.Nd4j;
|
||||
import org.datavec.api.transform.Operation;
|
||||
|
||||
import java.util.HashMap;
|
||||
import java.util.Map;
|
||||
|
||||
import static org.junit.Assert.*;
|
||||
|
||||
public class TransformProcessTest {
|
||||
@Test(expected = IllegalArgumentException.class)
|
||||
public void when_noChannelNameIsSuppliedToBuild_expect_exception() {
|
||||
// Arrange
|
||||
TransformProcess.builder().build();
|
||||
}
|
||||
|
||||
@Test(expected = IllegalArgumentException.class)
|
||||
public void when_callingTransformWithNullArg_expect_exception() {
|
||||
// Arrange
|
||||
TransformProcess sut = TransformProcess.builder()
|
||||
.build("test");
|
||||
|
||||
// Act
|
||||
sut.transform(null, 0, false);
|
||||
}
|
||||
|
||||
@Test(expected = IllegalArgumentException.class)
|
||||
public void when_callingTransformWithEmptyChannelData_expect_exception() {
|
||||
// Arrange
|
||||
TransformProcess sut = TransformProcess.builder()
|
||||
.build("test");
|
||||
Map<String, Object> channelsData = new HashMap<String, Object>();
|
||||
|
||||
// Act
|
||||
sut.transform(channelsData, 0, false);
|
||||
}
|
||||
|
||||
@Test(expected = NullPointerException.class)
|
||||
public void when_addingNullFilter_expect_nullException() {
|
||||
// Act
|
||||
TransformProcess.builder().filter(null);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_fileteredOut_expect_skippedObservationAndFollowingOperationsSkipped() {
|
||||
// Arrange
|
||||
IntegerTransformOperationMock transformOperationMock = new IntegerTransformOperationMock();
|
||||
TransformProcess sut = TransformProcess.builder()
|
||||
.filter(new FilterOperationMock(true))
|
||||
.transform("test", transformOperationMock)
|
||||
.build("test");
|
||||
Map<String, Object> channelsData = new HashMap<String, Object>() {{
|
||||
put("test", 1);
|
||||
}};
|
||||
|
||||
// Act
|
||||
Observation result = sut.transform(channelsData, 0, false);
|
||||
|
||||
// Assert
|
||||
assertTrue(result.isSkipped());
|
||||
assertFalse(transformOperationMock.isCalled);
|
||||
}
|
||||
|
||||
@Test(expected = NullPointerException.class)
|
||||
public void when_addingTransformOnNullChannel_expect_nullException() {
|
||||
// Act
|
||||
TransformProcess.builder().transform(null, new IntegerTransformOperationMock());
|
||||
}
|
||||
|
||||
@Test(expected = NullPointerException.class)
|
||||
public void when_addingTransformWithNullTransform_expect_nullException() {
|
||||
// Act
|
||||
TransformProcess.builder().transform("test", null);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_transformIsCalled_expect_channelDataTransformedInSameOrder() {
|
||||
// Arrange
|
||||
TransformProcess sut = TransformProcess.builder()
|
||||
.filter(new FilterOperationMock(false))
|
||||
.transform("test", new IntegerTransformOperationMock())
|
||||
.transform("test", new ToDataSetTransformOperationMock())
|
||||
.build("test");
|
||||
Map<String, Object> channelsData = new HashMap<String, Object>() {{
|
||||
put("test", 1);
|
||||
}};
|
||||
|
||||
// Act
|
||||
Observation result = sut.transform(channelsData, 0, false);
|
||||
|
||||
// Assert
|
||||
assertFalse(result.isSkipped());
|
||||
assertEquals(-1.0, result.getData().getDouble(0), 0.00001);
|
||||
}
|
||||
|
||||
@Test(expected = NullPointerException.class)
|
||||
public void when_addingPreProcessOnNullChannel_expect_nullException() {
|
||||
// Act
|
||||
TransformProcess.builder().preProcess(null, new DataSetPreProcessorMock());
|
||||
}
|
||||
|
||||
@Test(expected = NullPointerException.class)
|
||||
public void when_addingPreProcessWithNullTransform_expect_nullException() {
|
||||
// Act
|
||||
TransformProcess.builder().transform("test", null);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_preProcessIsCalled_expect_channelDataPreProcessedInSameOrder() {
|
||||
// Arrange
|
||||
TransformProcess sut = TransformProcess.builder()
|
||||
.filter(new FilterOperationMock(false))
|
||||
.transform("test", new IntegerTransformOperationMock())
|
||||
.transform("test", new ToDataSetTransformOperationMock())
|
||||
.preProcess("test", new DataSetPreProcessorMock())
|
||||
.build("test");
|
||||
Map<String, Object> channelsData = new HashMap<String, Object>() {{
|
||||
put("test", 1);
|
||||
}};
|
||||
|
||||
// Act
|
||||
Observation result = sut.transform(channelsData, 0, false);
|
||||
|
||||
// Assert
|
||||
assertFalse(result.isSkipped());
|
||||
assertEquals(1, result.getData().shape().length);
|
||||
assertEquals(1, result.getData().shape()[0]);
|
||||
assertEquals(-10.0, result.getData().getDouble(0), 0.00001);
|
||||
}
|
||||
|
||||
@Test(expected = IllegalStateException.class)
|
||||
public void when_transformingNullData_expect_exception() {
|
||||
// Arrange
|
||||
TransformProcess sut = TransformProcess.builder()
|
||||
.transform("test", new IntegerTransformOperationMock())
|
||||
.build("test");
|
||||
Map<String, Object> channelsData = new HashMap<String, Object>() {{
|
||||
put("test", 1);
|
||||
}};
|
||||
|
||||
// Act
|
||||
Observation result = sut.transform(channelsData, 0, false);
|
||||
}
|
||||
|
||||
@Test(expected = IllegalArgumentException.class)
|
||||
public void when_transformingAndChannelsNotDataSet_expect_exception() {
|
||||
// Arrange
|
||||
TransformProcess sut = TransformProcess.builder()
|
||||
.preProcess("test", new DataSetPreProcessorMock())
|
||||
.build("test");
|
||||
|
||||
// Act
|
||||
Observation result = sut.transform(null, 0, false);
|
||||
}
|
||||
|
||||
|
||||
@Test(expected = IllegalArgumentException.class)
|
||||
public void when_transformingAndChannelsEmptyDataSet_expect_exception() {
|
||||
// Arrange
|
||||
TransformProcess sut = TransformProcess.builder()
|
||||
.preProcess("test", new DataSetPreProcessorMock())
|
||||
.build("test");
|
||||
Map<String, Object> channelsData = new HashMap<String, Object>();
|
||||
|
||||
// Act
|
||||
Observation result = sut.transform(channelsData, 0, false);
|
||||
}
|
||||
|
||||
@Test(expected = IllegalArgumentException.class)
|
||||
public void when_buildIsCalledWithoutChannelNames_expect_exception() {
|
||||
// Act
|
||||
TransformProcess.builder().build();
|
||||
}
|
||||
|
||||
@Test(expected = NullPointerException.class)
|
||||
public void when_buildIsCalledWithNullChannelName_expect_exception() {
|
||||
// Act
|
||||
TransformProcess.builder().build(null);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_resetIsCalled_expect_resettableAreReset() {
|
||||
// Arrange
|
||||
ResettableTransformOperationMock resettableOperation = new ResettableTransformOperationMock();
|
||||
TransformProcess sut = TransformProcess.builder()
|
||||
.filter(new FilterOperationMock(false))
|
||||
.transform("test", new IntegerTransformOperationMock())
|
||||
.transform("test", resettableOperation)
|
||||
.build("test");
|
||||
|
||||
// Act
|
||||
sut.reset();
|
||||
|
||||
// Assert
|
||||
assertTrue(resettableOperation.isResetCalled);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_buildIsCalledAndAllChannelsAreDataSets_expect_observation() {
|
||||
// Arrange
|
||||
TransformProcess sut = TransformProcess.builder()
|
||||
.transform("test", new ToDataSetTransformOperationMock())
|
||||
.build("test");
|
||||
Map<String, Object> channelsData = new HashMap<String, Object>() {{
|
||||
put("test", 1);
|
||||
}};
|
||||
|
||||
// Act
|
||||
Observation result = sut.transform(channelsData, 123, true);
|
||||
|
||||
// Assert
|
||||
assertFalse(result.isSkipped());
|
||||
|
||||
assertEquals(1.0, result.getData().getDouble(0), 0.00001);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_buildIsCalledAndAllChannelsAreINDArrays_expect_observation() {
|
||||
// Arrange
|
||||
TransformProcess sut = TransformProcess.builder()
|
||||
.build("test");
|
||||
Map<String, Object> channelsData = new HashMap<String, Object>() {{
|
||||
put("test", Nd4j.create(new double[] { 1.0 }));
|
||||
}};
|
||||
|
||||
// Act
|
||||
Observation result = sut.transform(channelsData, 123, true);
|
||||
|
||||
// Assert
|
||||
assertFalse(result.isSkipped());
|
||||
|
||||
assertEquals(1.0, result.getData().getDouble(0), 0.00001);
|
||||
}
|
||||
|
||||
@Test(expected = IllegalStateException.class)
|
||||
public void when_buildIsCalledAndChannelsNotDataSetsOrINDArrays_expect_exception() {
|
||||
// Arrange
|
||||
TransformProcess sut = TransformProcess.builder()
|
||||
.build("test");
|
||||
Map<String, Object> channelsData = new HashMap<String, Object>() {{
|
||||
put("test", 1);
|
||||
}};
|
||||
|
||||
// Act
|
||||
Observation result = sut.transform(channelsData, 123, true);
|
||||
}
|
||||
|
||||
@Test(expected = NullPointerException.class)
|
||||
public void when_channelDataIsNull_expect_exception() {
|
||||
// Arrange
|
||||
TransformProcess sut = TransformProcess.builder()
|
||||
.transform("test", new IntegerTransformOperationMock())
|
||||
.build("test");
|
||||
Map<String, Object> channelsData = new HashMap<String, Object>() {{
|
||||
put("test", null);
|
||||
}};
|
||||
|
||||
// Act
|
||||
sut.transform(channelsData, 0, false);
|
||||
}
|
||||
|
||||
@Test(expected = IllegalArgumentException.class)
|
||||
public void when_transformAppliedOnChannelNotInMap_expect_exception() {
|
||||
// Arrange
|
||||
TransformProcess sut = TransformProcess.builder()
|
||||
.transform("test", new IntegerTransformOperationMock())
|
||||
.build("test");
|
||||
Map<String, Object> channelsData = new HashMap<String, Object>() {{
|
||||
put("not-test", 1);
|
||||
}};
|
||||
|
||||
// Act
|
||||
sut.transform(channelsData, 0, false);
|
||||
}
|
||||
|
||||
@Test(expected = IllegalArgumentException.class)
|
||||
public void when_preProcessAppliedOnChannelNotInMap_expect_exception() {
|
||||
// Arrange
|
||||
TransformProcess sut = TransformProcess.builder()
|
||||
.preProcess("test", new DataSetPreProcessorMock())
|
||||
.build("test");
|
||||
Map<String, Object> channelsData = new HashMap<String, Object>() {{
|
||||
put("not-test", 1);
|
||||
}};
|
||||
|
||||
// Act
|
||||
sut.transform(channelsData, 0, false);
|
||||
}
|
||||
|
||||
@Test(expected = IllegalArgumentException.class)
|
||||
public void when_buildContainsChannelNotInMap_expect_exception() {
|
||||
// Arrange
|
||||
TransformProcess sut = TransformProcess.builder()
|
||||
.transform("test", new IntegerTransformOperationMock())
|
||||
.build("not-test");
|
||||
Map<String, Object> channelsData = new HashMap<String, Object>() {{
|
||||
put("test", 1);
|
||||
}};
|
||||
|
||||
// Act
|
||||
sut.transform(channelsData, 0, false);
|
||||
}
|
||||
|
||||
@Test(expected = IllegalArgumentException.class)
|
||||
public void when_preProcessNotAppliedOnDataSet_expect_exception() {
|
||||
// Arrange
|
||||
TransformProcess sut = TransformProcess.builder()
|
||||
.preProcess("test", new DataSetPreProcessorMock())
|
||||
.build("test");
|
||||
Map<String, Object> channelsData = new HashMap<String, Object>() {{
|
||||
put("test", 1);
|
||||
}};
|
||||
|
||||
// Act
|
||||
sut.transform(channelsData, 0, false);
|
||||
}
|
||||
|
||||
private static class FilterOperationMock implements FilterOperation {
|
||||
|
||||
private final boolean skipped;
|
||||
|
||||
public FilterOperationMock(boolean skipped) {
|
||||
this.skipped = skipped;
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean isSkipped(Map<String, Object> channelsData, int currentObservationStep, boolean isFinalObservation) {
|
||||
return skipped;
|
||||
}
|
||||
}
|
||||
|
||||
private static class IntegerTransformOperationMock implements Operation<Integer, Integer> {
|
||||
|
||||
public boolean isCalled = false;
|
||||
|
||||
@Override
|
||||
public Integer transform(Integer input) {
|
||||
isCalled = true;
|
||||
return -input;
|
||||
}
|
||||
}
|
||||
|
||||
private static class ToDataSetTransformOperationMock implements Operation<Integer, DataSet> {
|
||||
|
||||
@Override
|
||||
public DataSet transform(Integer input) {
|
||||
return new org.nd4j.linalg.dataset.DataSet(Nd4j.create(new double[] { input }), null);
|
||||
}
|
||||
}
|
||||
|
||||
private static class ResettableTransformOperationMock implements Operation<Integer, Integer>, ResettableOperation {
|
||||
|
||||
private boolean isResetCalled = false;
|
||||
|
||||
@Override
|
||||
public Integer transform(Integer input) {
|
||||
return input * 10;
|
||||
}
|
||||
|
||||
@Override
|
||||
public void reset() {
|
||||
isResetCalled = true;
|
||||
}
|
||||
}
|
||||
|
||||
private static class DataSetPreProcessorMock implements DataSetPreProcessor {
|
||||
|
||||
@Override
|
||||
public void preProcess(DataSet dataSet) {
|
||||
dataSet.getFeatures().muli(10.0);
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,54 @@
|
||||
package org.deeplearning4j.rl4j.observation.transform.filter;
|
||||
|
||||
import org.deeplearning4j.rl4j.observation.transform.FilterOperation;
|
||||
import org.junit.Test;
|
||||
|
||||
import static org.junit.Assert.assertFalse;
|
||||
import static org.junit.Assert.assertTrue;
|
||||
|
||||
public class UniformSkippingFilterTest {
|
||||
|
||||
@Test(expected = IllegalArgumentException.class)
|
||||
public void when_negativeSkipFrame_expect_exception() {
|
||||
// Act
|
||||
new UniformSkippingFilter(-1);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_skippingIs4_expect_firstNotSkippedOther3Skipped() {
|
||||
// Assemble
|
||||
FilterOperation sut = new UniformSkippingFilter(4);
|
||||
boolean[] isSkipped = new boolean[8];
|
||||
|
||||
// Act
|
||||
for(int i = 0; i < 8; ++i) {
|
||||
isSkipped[i] = sut.isSkipped(null, i, false);
|
||||
}
|
||||
|
||||
// Assert
|
||||
assertFalse(isSkipped[0]);
|
||||
assertTrue(isSkipped[1]);
|
||||
assertTrue(isSkipped[2]);
|
||||
assertTrue(isSkipped[3]);
|
||||
|
||||
assertFalse(isSkipped[4]);
|
||||
assertTrue(isSkipped[5]);
|
||||
assertTrue(isSkipped[6]);
|
||||
assertTrue(isSkipped[7]);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_isLastObservation_expect_observationNotSkipped() {
|
||||
// Assemble
|
||||
FilterOperation sut = new UniformSkippingFilter(4);
|
||||
|
||||
// Act
|
||||
boolean isSkippedNotLastObservation = sut.isSkipped(null, 1, false);
|
||||
boolean isSkippedLastObservation = sut.isSkipped(null, 1, true);
|
||||
|
||||
// Assert
|
||||
assertTrue(isSkippedNotLastObservation);
|
||||
assertFalse(isSkippedLastObservation);
|
||||
}
|
||||
|
||||
}
|
||||
@ -0,0 +1,31 @@
|
||||
package org.deeplearning4j.rl4j.observation.transform.operation;
|
||||
|
||||
import org.deeplearning4j.rl4j.helper.INDArrayHelper;
|
||||
import org.junit.Test;
|
||||
import org.nd4j.linalg.api.ndarray.INDArray;
|
||||
import org.nd4j.linalg.factory.Nd4j;
|
||||
|
||||
import static org.junit.Assert.*;
|
||||
|
||||
public class SimpleNormalizationTransformTest {
|
||||
@Test(expected = IllegalArgumentException.class)
|
||||
public void when_maxIsLessThanMin_expect_exception() {
|
||||
// Arrange
|
||||
SimpleNormalizationTransform sut = new SimpleNormalizationTransform(10.0, 1.0);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_transformIsCalled_expect_inputNormalized() {
|
||||
// Arrange
|
||||
SimpleNormalizationTransform sut = new SimpleNormalizationTransform(1.0, 11.0);
|
||||
INDArray input = Nd4j.create(new double[] { 1.0, 11.0 });
|
||||
|
||||
// Act
|
||||
INDArray result = sut.transform(input);
|
||||
|
||||
// Assert
|
||||
assertEquals(0.0, result.getDouble(0), 0.00001);
|
||||
assertEquals(1.0, result.getDouble(1), 0.00001);
|
||||
}
|
||||
|
||||
}
|
||||
@ -23,15 +23,18 @@ import org.deeplearning4j.nn.gradient.Gradient;
|
||||
import org.deeplearning4j.nn.graph.ComputationGraph;
|
||||
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
|
||||
import org.deeplearning4j.rl4j.learning.IHistoryProcessor;
|
||||
import org.deeplearning4j.rl4j.learning.Learning;
|
||||
import org.deeplearning4j.rl4j.learning.sync.qlearning.QLearning;
|
||||
import org.deeplearning4j.rl4j.learning.sync.qlearning.discrete.QLearningDiscreteTest;
|
||||
import org.deeplearning4j.rl4j.mdp.MDP;
|
||||
import org.deeplearning4j.rl4j.network.NeuralNet;
|
||||
import org.deeplearning4j.rl4j.network.ac.IActorCritic;
|
||||
import org.deeplearning4j.rl4j.observation.Observation;
|
||||
import org.deeplearning4j.rl4j.space.ActionSpace;
|
||||
import org.deeplearning4j.rl4j.space.DiscreteSpace;
|
||||
import org.deeplearning4j.rl4j.space.Encodable;
|
||||
import org.deeplearning4j.rl4j.support.*;
|
||||
import org.deeplearning4j.rl4j.util.LegacyMDPWrapper;
|
||||
import org.junit.Test;
|
||||
import org.nd4j.linalg.activations.Activation;
|
||||
import org.nd4j.linalg.api.ndarray.INDArray;
|
||||
@ -186,8 +189,8 @@ public class PolicyTest {
|
||||
QLearning.QLConfiguration conf = new QLearning.QLConfiguration(0, 0, 0, 5, 1, 0,
|
||||
0, 1.0, 0, 0, 0, 0, true);
|
||||
MockNeuralNet nnMock = new MockNeuralNet();
|
||||
MockRefacPolicy sut = new MockRefacPolicy(nnMock);
|
||||
IHistoryProcessor.Configuration hpConf = new IHistoryProcessor.Configuration(5, 4, 4, 4, 4, 0, 0, 2);
|
||||
MockRefacPolicy sut = new MockRefacPolicy(nnMock, observationSpace.getShape(), hpConf.getSkipFrame(), hpConf.getHistoryLength());
|
||||
MockHistoryProcessor hp = new MockHistoryProcessor(hpConf);
|
||||
|
||||
// Act
|
||||
@ -197,13 +200,6 @@ public class PolicyTest {
|
||||
assertEquals(1, nnMock.resetCallCount);
|
||||
assertEquals(465.0, totalReward, 0.0001);
|
||||
|
||||
// HistoryProcessor
|
||||
assertEquals(16, hp.addCalls.size());
|
||||
assertEquals(31, hp.recordCalls.size());
|
||||
for(int i=0; i <= 30; ++i) {
|
||||
assertEquals((double)i, hp.recordCalls.get(i).getDouble(0), 0.0001);
|
||||
}
|
||||
|
||||
// MDP
|
||||
assertEquals(1, mdp.resetCount);
|
||||
assertEquals(30, mdp.actions.size());
|
||||
@ -219,10 +215,15 @@ public class PolicyTest {
|
||||
public static class MockRefacPolicy extends Policy<MockEncodable, Integer> {
|
||||
|
||||
private NeuralNet neuralNet;
|
||||
private final int[] shape;
|
||||
private final int skipFrame;
|
||||
private final int historyLength;
|
||||
|
||||
public MockRefacPolicy(NeuralNet neuralNet) {
|
||||
|
||||
public MockRefacPolicy(NeuralNet neuralNet, int[] shape, int skipFrame, int historyLength) {
|
||||
this.neuralNet = neuralNet;
|
||||
this.shape = shape;
|
||||
this.skipFrame = skipFrame;
|
||||
this.historyLength = historyLength;
|
||||
}
|
||||
|
||||
@Override
|
||||
@ -239,5 +240,11 @@ public class PolicyTest {
|
||||
public Integer nextAction(INDArray input) {
|
||||
return (int)input.getDouble(0);
|
||||
}
|
||||
|
||||
@Override
|
||||
protected <AS extends ActionSpace<Integer>> Learning.InitMdp<Observation> refacInitMdp(LegacyMDPWrapper<MockEncodable, Integer, AS> mdpWrapper, IHistoryProcessor hp, RefacEpochStepCounter epochStepCounter) {
|
||||
mdpWrapper.setTransformProcess(MockMDP.buildTransformProcess(shape, skipFrame, historyLength));
|
||||
return super.refacInitMdp(mdpWrapper, hp, epochStepCounter);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -2,6 +2,12 @@ package org.deeplearning4j.rl4j.support;
|
||||
|
||||
import org.deeplearning4j.gym.StepReply;
|
||||
import org.deeplearning4j.rl4j.mdp.MDP;
|
||||
import org.deeplearning4j.rl4j.observation.transform.TransformProcess;
|
||||
import org.deeplearning4j.rl4j.observation.transform.filter.UniformSkippingFilter;
|
||||
import org.deeplearning4j.rl4j.observation.transform.legacy.EncodableToINDArrayTransform;
|
||||
import org.deeplearning4j.rl4j.observation.transform.operation.HistoryMergeTransform;
|
||||
import org.deeplearning4j.rl4j.observation.transform.operation.SimpleNormalizationTransform;
|
||||
import org.deeplearning4j.rl4j.observation.transform.operation.historymerge.CircularFifoStore;
|
||||
import org.deeplearning4j.rl4j.space.DiscreteSpace;
|
||||
import org.deeplearning4j.rl4j.space.ObservationSpace;
|
||||
import org.nd4j.linalg.api.rng.Random;
|
||||
@ -77,4 +83,16 @@ public class MockMDP implements MDP<MockEncodable, Integer, DiscreteSpace> {
|
||||
public MDP newInstance() {
|
||||
return null;
|
||||
}
|
||||
|
||||
public static TransformProcess buildTransformProcess(int[] shape, int skipFrame, int historyLength) {
|
||||
return TransformProcess.builder()
|
||||
.filter(new UniformSkippingFilter(skipFrame))
|
||||
.transform("data", new EncodableToINDArrayTransform(shape))
|
||||
.transform("data", new SimpleNormalizationTransform(0.0, 255.0))
|
||||
.transform("data", HistoryMergeTransform.builder()
|
||||
.elementStore(new CircularFifoStore(historyLength))
|
||||
.build())
|
||||
.build("data");
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user