rlberry - A Reinforcement Learning Library for Research and Education

Improving interface and tools for parallel execution (#50)

• AgentStats renamed to AgentManager.
• AgentManager can handle agents that cannot be pickled.
• Agent interface requires eval() method instead of policy() to handle more general agents (e.g. reward-free, POMDPs etc).
• Multi-processing and multi-threading are now done with ProcessPoolExecutor and ThreadPoolExecutor (allowing nested processes for example). Processes are created with spawn (jax does not work with fork, see #51).

New experimental features (see #51, #62)

• JAX implementation of DQN and replay buffer using reverb.
• rlberry.network: server and client interfaces to exchange messages via sockets.
• RemoteAgentManager to train agents in a remote server and gather the results locally (using rlberry.network).

Logging and rendering:

• Data logging with a new DefaultWriter and improved evaluation and plot methods in rlberry.manager.evaluation.
• Fix rendering bug with OpenGL (bf606b44aaba1b918daf3dcc02be96a8ef5436b4).

Bug fixes.