pytorch-soft-actor-critic/README.md

Description

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Reimplementation of Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor.

Contributions are welcome. If you find any mistake (very likely) or know how to make it more stable, don't hesitate to send a pull request.

Requirements

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• mujoco-py
• Plotly
• PyTorch

Run

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Use the default hyperparameters.

For SAC (Gaussian Policy):

python main.py --algo SAC --env-name HalfCheetah-v2

For SAC (Gaussian Mixture Policy):

python main.py --algo SAC(GMM) --env-name HalfCheetah-v2 --k 4

TODO

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• Gaussian Policy
• Reparameterization
• Gaussian Mixture Model
• Use 2 Q-functions
• Deterministic Policy
• Soft Actor-Critic (hard target update)
• Evaluate the trained Policy