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Sac Walker2d V3
This is a reinforcement learning model based on the SAC algorithm, specifically designed for the Walker2d-v3 environment to control bipedal robot walking.
Downloads 43
Release Time : 6/2/2022
Model Overview
The model is trained using the SAC algorithm from the stable-baselines3 library to solve continuous control tasks in the Walker2d-v3 environment, achieving stable walking for bipedal robots.
Model Features
Efficient Continuous Control
Uses the SAC algorithm to optimize control policies in continuous action spaces.
Stable Training
The model exhibits stable learning curves during training.
RL Zoo Integration
Seamlessly integrates with the RL Zoo training framework for hyperparameter optimization and model sharing.
Model Capabilities
Bipedal Robot Control
Continuous Action Space Optimization
Reinforcement Learning Policy Training
Use Cases
Robot Control
Bipedal Robot Walking
Controls a bipedal robot to achieve stable walking in a simulated environment.
Average reward reaches 3876.28 +/- 75.51
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