51 lines
1.6 KiB
Java
51 lines
1.6 KiB
Java
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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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.mdp;
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import org.deeplearning4j.gym.StepReply;
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import org.deeplearning4j.rl4j.space.ActionSpace;
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import org.deeplearning4j.rl4j.space.ObservationSpace;
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/**
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* @author rubenfiszel (ruben.fiszel@epfl.ch) 7/12/16.
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* An interface that ensure an environment is expressible as a
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* Markov Decsision Process. This implementation follow the gym model.
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* It works based on side effects which is perfect for imperative simulation.
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*
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* A bit sad that it doesn't use efficiently stateful mdp that could be rolled back
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* in a "functionnal manner" if step return a mdp
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*
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*/
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public interface MDP<O, A, AS extends ActionSpace<A>> {
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ObservationSpace<O> getObservationSpace();
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AS getActionSpace();
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O reset();
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void close();
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StepReply<O> step(A action);
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boolean isDone();
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MDP<O, A, AS> newInstance();
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}
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