PPO HalfCheetah-v3 Model
| Property | Value |
|---|---|
| Framework | stable-baselines3 |
| Algorithm | Proximal Policy Optimization (PPO) |
| Environment | HalfCheetah-v3 |
| Mean Reward | 5836.27 ±171.68 |
What is ppo-HalfCheetah-v3?
This is a reinforcement learning model implementing the PPO algorithm to control the HalfCheetah-v3 environment. The model has been trained using the stable-baselines3 library and achieves impressive locomotion control with a mean reward of 5836.27. It utilizes deep neural networks with specific architecture optimizations for both policy and value functions.
Implementation Details
The model employs a sophisticated neural network architecture with separate policy and value function networks, each containing two hidden layers of 256 units. Key hyperparameters include a batch size of 64, clip range of 0.1, and carefully tuned learning rate of 2.0633e-05.
- MLP Policy with orthogonal initialization disabled
- ReLU activation functions
- Normalized observations with reward normalization disabled
- 20 training epochs with 512 steps per update
Core Capabilities
- Efficient locomotion control in the HalfCheetah environment
- Stable training with GAE-Lambda value of 0.92
- Optimized exploration-exploitation balance with entropy coefficient of 0.000401762
- Robust performance with gradient norm clipping at 0.8
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its carefully tuned hyperparameters and normalized observation space, resulting in stable and high-performing locomotion control. The specific combination of network architecture and PPO parameters has been optimized for the HalfCheetah-v3 environment.
Q: What are the recommended use cases?
The model is specifically designed for continuous control tasks in the HalfCheetah environment. It's ideal for researchers and developers working on locomotion tasks in robotics simulation, particularly those requiring stable and efficient movement patterns.
