Crate border

A reinforcement learning library in Rust.

Border consists of the following crates:

• Core and utility
  • border-core (doc) provides basic traits and functions for environments and reinforcement learning (RL) agents.
  • border-tensorboard (doc) implements the TensorboardRecorder struct for writing records that can be visualized in Tensorboard, based on tensorboard-rs.
  • border-mlflow-tracking (doc) provides MLflow tracking support for logging metrics during training via REST API.
  • border-async-trainer (doc) defines traits and functions for asynchronous training of RL agents using multiple actors. Each actor runs a sampling process in parallel, where an agent interacts with an environment to collect samples for a shared replay buffer.
  • border serves as a collection of examples.
• Environment
  • border-py-gym-env (doc) provides a wrapper for Gymnasium environments written in Python.
  • border-atari-env (doc) implements a wrapper for atari-env, which is part of gym-rs.
  • border-minari (doc) provides a wrapper for Minari.
• Agent
  • border-tch-agent (doc) implements RL agents based on tch, including Deep Q Network (DQN), Implicit Quantile Network (IQN), and Soft Actor-Critic (SAC).
  • border-candle-agent (doc) implements RL agents based on candle.
  • border-policy-no-backend (doc) implements policies that are independent of any deep learning backend, such as Torch.

Status

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

Examples

Example scripts are available in the border/examples directory. These have been tested in Docker containers, specifically using the aarch64 configuration on an M2 MacBook Air. Some scripts require several days for the training process, as tested on an Ubuntu 22.04 virtual machine in GPUSOROBAN, a computing cloud.

Docker

Docker configuration files for development and testing are available in the dev-border repository. These files are used to set up the development environment, supporting both aarch64 (e.g., M2 MacBook Air) and amd64 architectures.

License

Crates	License
border-core	MIT OR Apache-2.0
border-tensorboard	MIT OR Apache-2.0
border-mlflow-tracking	MIT OR Apache-2.0
border-async-trainer	MIT OR Apache-2.0
border-py-gym-env	MIT OR Apache-2.0
border-atari-env	GPL-2.0-or-later
border-minari	MIT OR Apache-2.0
border-tch-agent	MIT OR Apache-2.0
border-candle-agent	MIT OR Apache-2.0
border-policy-no-backend	MIT OR Apache-2.0
border	GPL-2.0-or-later