Representation-based Reinforcement Learning

This repo contains implementations for RL with:

• Latent Variable Representations (LV), as outlined in [1].
• Contrastive Representations (CTRL), as described in [2].

Directory

• agent hosts implementation files for various agents, including the Soft Actor-Critic baseline (sac), SAC with Latent Variable (vlsac), and SAC with Contrastive Representations (ctrlsac).
• networks contains base implementations for critics, policy networks, variational autoencoders (VAE), and more.
• utils comprises replay buffers and several auxiliary functions.

Run

Execute the main.py script with your preferred arguments, such as --alg for algorithm type, --env for environment, and so on.

Example usage: python main.py --alg vlsac --env HalfCheetah-v3.

References

[1] Ren, Tongzheng, Chenjun Xiao, Tianjun Zhang, Na Li, Zhaoran Wang, Sujay Sanghavi, Dale Schuurmans, and Bo Dai. "Latent variable representation for reinforcement learning." arXiv preprint arXiv:2212.08765 (2022).

[2] Zhang, Tianjun, Tongzheng Ren, Mengjiao Yang, Joseph Gonzalez, Dale Schuurmans, and Bo Dai. "Making linear mdps practical via contrastive representation learning." In International Conference on Machine Learning, pp. 26447-26466. PMLR, 2022.