Experimenting Reinforcement Learning with Rust Burn

Training on CartPole

Agents

The project implements the following algorithms:

• Deep Q-Network (DQN)
• Proximal Policy Optimization (PPO)
• Soft Actor-Critic for Discrete Action (SAC-Discrete)

Environment

This project uses gym-rs for simulating environments. Users can create their own environment by implementing the Environment trait.

References

• PyTorch RL tutorial
• PPO with TorchRL tutorial
• Christodoulou, P. (2019). Soft actor-critic for discrete action settings. arXiv preprint arXiv:1910.07207.