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 :

python main.py --env-name Humanoid-v2 --scale_R 20 

For SAC (Hard Update):

python main.py --env-name Humanoid-v2 --scale_R 20 --tau 1 --value_update 1000

For SAC (Deterministic, Hard Update):

python main.py --env-name Humanoid-v2 --scale_R 20 --deterministic True --tau 1 --value_update 1000

Results

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My results on Humanoid-v2 environment using SAC, SAC(hard update) and SAC(deterministic, hard update). This is a plot of average rewards at every 10000 step interval