Decision Transformer for Gym Hopper Expert
| Property | Value |
|---|---|
| Author | edbeeching |
| Model Type | Decision Transformer |
| Environment | Gym Hopper |
| Model URL | Hugging Face |
What is decision-transformer-gym-hopper-expert?
This is a specialized Decision Transformer model trained specifically for the Gym Hopper environment using expert trajectories. The model implements a sophisticated approach to learning optimal control policies for a one-legged hopping robot simulation, utilizing normalized state-action pairs for enhanced performance.
Implementation Details
The model operates using specific normalization coefficients for 11 different parameters, incorporating both mean and standard deviation values for precise state representation. These normalizations are crucial for processing the input state space and generating appropriate actions for the hopping task.
- Utilizes 11-dimensional state space normalization
- Implements expert trajectory sampling
- Features precise mean and standard deviation coefficients for state normalization
Core Capabilities
- Expert-level performance in hopping tasks
- Robust state space handling through normalization
- Efficient trajectory following and action generation
- Seamless integration with Gym Hopper environment
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its specialized training on expert trajectories and its precise normalization scheme, making it particularly effective for the Gym Hopper environment. The implementation includes carefully calibrated normalization coefficients that enable optimal performance in hopping tasks.
Q: What are the recommended use cases?
The model is specifically designed for controlling a hopping robot in the Gym Hopper environment. It's ideal for researchers and developers working on robotic control systems, reinforcement learning applications, and motion planning in similar dynamic environments.
