An easy-to-use 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).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)
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:
DefaultWriter
and improved evaluation and plot methods in rlberry.manager.evaluation
.Bug fixes.