109 lines
3.4 KiB
Markdown
109 lines
3.4 KiB
Markdown
# gym-java-client
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A java http client for [gym-http-api](https://github.com/openai/gym-http-api).
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Note: If you are encountering errors as reported in [issue #13](https://github.com/deeplearning4j/gym-java-client/issues/13), please execute the following command before launching `python gym_http_server.py`:
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```bash
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$ sudo sysctl -w net.ipv4.tcp_tw_recycle=1
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```
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# Quickstart
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To create a new Client, use the ClientFactory. If the url is not localhost:5000, provide it as a second argument
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```java
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Client<Box, Integer, DiscreteSpace> client = ClientFactory.build("CartPole-v0");
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```
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"CartPole-v0" is the name of the gym environment.
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The type parameters of a client are the Observation type, the Action type, the Observation Space type and the ActionSpace type.
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It is a bit cumbersome to both declare an ActionSpace and an Action since an ActionSpace knows what type is an Action but unfortunately java does't support type member and path dependant types.
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Here we use Box and BoxSpace for the environment and Integer and Discrete Space because it is how [CartPole-v0](https://gym.openai.com/envs/CartPole-v0) is specified.
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The methods nomenclature follows closely the api interface of [gym-http-api](https://github.com/openai/gym-http-api#api-specification), O is Observation an A is Action:
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```java
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//Static methods
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/**
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* @param url url of the server
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* @return set of all environments running on the server at the url
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*/
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public static Set<String> listAll(String url);
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/**
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* Shutdown the server at the url
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*
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* @param url url of the server
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*/
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public static void serverShutdown(String url);
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//Methods accessible from a Client
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/**
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* @return set of all environments running on the same server than this client
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*/
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public Set<String> listAll();
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/**
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* Step the environment by one action
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*
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* @param action action to step the environment with
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* @return the StepReply containing the next observation, the reward, if it is a terminal state and optional information.
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*/
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public StepReply<O> step(A action);
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/**
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* Reset the state of the environment and return an initial observation.
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*
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* @return initial observation
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*/
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public O reset();
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/**
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* Start monitoring.
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*
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* @param directory path to directory in which store the monitoring file
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* @param force clear out existing training data from this directory (by deleting every file prefixed with "openaigym.")
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* @param resume retain the training data already in this directory, which will be merged with our new data
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*/
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public void monitorStart(String directory, boolean force, boolean resume);
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/**
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* Flush all monitor data to disk
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*/
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public void monitorClose();
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/**
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* Upload monitoring data to OpenAI servers.
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*
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* @param trainingDir directory that contains the monitoring data
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* @param apiKey personal OpenAI API key
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* @param algorithmId an arbitrary string indicating the paricular version of the algorithm (including choices of parameters) you are running.
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**/
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public void upload(String trainingDir, String apiKey, String algorithmId);
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/**
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* Upload monitoring data to OpenAI servers.
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*
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* @param trainingDir directory that contains the monitoring data
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* @param apiKey personal OpenAI API key
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*/
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public void upload(String trainingDir, String apiKey);
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/**
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* Shutdown the server at the same url than this client
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*/
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public void serverShutdown()
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```
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## TODO
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* Add all ObservationSpace and ActionSpace when they will be available.
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