Expand description
A reinforcement learning library.
This library defines a set of environments and learning agents and simulates their interaction.
Environments implement the Environment trait, which has
associated observation, action, and state types.
Agents implement Agent and provide Actors that generate actions
in response to environment observations.
Agents can learn via the BatchUpdate trait.
Agent traits are generic over the observation (O) and action (A) types of the environment.
The 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
spaces.
Environment-actor simulation is performed by Steps and the
resulting Step are accessible via an Iterator interface.
Training is performed by train_serial and
train_parallel.
Re-exports
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;Modules
Reinforcement learning agents
Reinforcement learning environments
Agent-environment feedback
Logging statistics from simulation runs
Simulating agent-environment interaction
Spaces: runtime-defined types
Torch components
General-purpose utilities
Structs
Iterator of environment-actor steps.
Traits
Build a StructuredEnvironment.
Functions
Train a batch learning agent in parallel across several threads.
Train a batch learning agent in this thread.
Type Definitions
Pseudo-random number generator type used by agents and environments in this crate.