sac-Humanoid-v3
| Property | Value |
|---|---|
| Framework | Stable-baselines3 |
| Environment | Humanoid-v3 |
| Training Steps | 2,000,000 |
| Algorithm | Soft Actor-Critic (SAC) |
| Model URL | Hugging Face |
What is sac-Humanoid-v3?
sac-Humanoid-v3 is a reinforcement learning model trained using the Soft Actor-Critic (SAC) algorithm to control a humanoid robot in a simulated environment. The model is trained to perform complex bipedal locomotion tasks, learning to walk and maintain balance efficiently.
Implementation Details
The model is implemented using the stable-baselines3 library and trained through the RL Zoo framework. It utilizes an MlpPolicy (Multi-layer Perceptron Policy) architecture and begins learning after 10,000 initial steps. The training process extends to 2 million timesteps without normalization.
- Learning starts at 10,000 steps for initial exploration
- Uses MlpPolicy for neural network architecture
- Training conducted through RL Zoo framework
- No normalization applied during training
Core Capabilities
- Bipedal locomotion in complex environments
- Balance maintenance and stability control
- Adaptive movement strategies
- Real-time decision making for humanoid control
Frequently Asked Questions
Q: What makes this model unique?
This model implements the SAC algorithm, which is particularly effective for continuous control tasks like humanoid locomotion. It combines off-policy training with maximum entropy reinforcement learning, making it both sample-efficient and stable during training.
Q: What are the recommended use cases?
The model is ideal for research in bipedal robotics, simulation environments requiring humanoid control, and as a baseline for comparing humanoid locomotion algorithms. It can be used through the RL Zoo framework for both training and evaluation purposes.
