A reinforcement learning library.
Environments implement the
Environment trait, which has
associated observation, action, and state types.
Agent and provide
Actors that generate actions
in response to environment observations.
Agents can learn via the
Agent traits are generic over the observation (
O) and action (
A) types of the environment.
EnvStructure trait provides more details about possible values for these types via the
Space trait. A
Space can be thought of as a runtime-defined type,
describing a set of possible values while methods are provided by other traits in
pub use agents::Actor;
pub use agents::Agent;
pub use agents::BatchUpdate;
pub use agents::BuildAgent;
pub use envs::EnvStructure;
pub use envs::Environment;
pub use simulation::Simulation;
pub use simulation::Step;
pub use simulation::StepsIter;
Reinforcement learning agents
Reinforcement learning environments
Logging statistics from simulation runs
Simulating agent-environment interaction
Spaces: runtime-defined types
Iterator of environment-actor steps.
Train a batch learning agent in parallel across several threads.
Train a batch learning agent in this thread.
Pseudo-random number generator type used by agents and environments in this crate.