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.

Border is experimental and currently under development. API is unstable.

There are some example sctipts in border/examples directory. These are tested in Docker containers, speficically the one in aarch64 directory on M2 Macbook air. Some scripts take few days for the training process, tested on Ubuntu22.04 virtual machine in GPUSOROBAN, a computing cloud.

In docker directory, there are scripts for running a Docker container, in which you can try the examples described above. Currently, only aarch64 is mainly used for the development.