Crate border

A reinforcement learning library in Rust.

Border consists of the following crates:

• Core and utility
  • border-core provides basic traits and functions generic to environments and reinforcmenet learning (RL) agents.
  • border-tensorboard has TensorboardRecorder struct to write records which can be shown in Tensorboard. It is based on tensorboard-rs.
  • border-mlflow-tracking support MLflow tracking to log metrices during training via REST API.
  • border-async-trainer defines some traits and functions for asynchronous training of RL agents by multiple actors, which runs sampling processes in parallel. In each sampling process, an agent interacts with an environment to collect samples to be sent to a shared replay buffer.
  • border is just a collection of examples.
• Environment
  • border-py-gym-env is a wrapper of the Gymnasium environments written in Python.
  • border-atari-env is a wrapper of atari-env, which is a part of gym-rs.
• Agent
  • border-tch-agent includes RL agents based on tch, including Deep Q network (DQN), implicit quantile network (IQN), and soft actor critic (SAC).
  • border-candle-agent includes RL agents based on candle
  • border-policy-no-backend includes a policy that is independent of any deep learning backend, such as Torch.

Modules

util

Utilities