Decision Transformer for Gym Hopper
| Property | Value |
|---|---|
| Author | edbeeching |
| Model Type | Decision Transformer |
| Environment | Gym Hopper |
| Training Data | Medium Trajectories |
| Model URL | Hugging Face |
What is decision-transformer-gym-hopper-medium?
This is a specialized Decision Transformer model designed specifically for the Gym Hopper environment, trained on medium-quality trajectories. The model implements a sophisticated approach to reinforcement learning by converting the sequential decision-making process into a sequence modeling problem.
Implementation Details
The model requires specific normalization coefficients for optimal performance, including 11-dimensional mean and standard deviation vectors. These normalization parameters are crucial for preprocessing the input data and ensuring consistent model behavior.
- 11-dimensional state space with precisely defined normalization coefficients
- Trained on medium-quality trajectory data
- Integrated with the Gym Hopper environment for robotics control
Core Capabilities
- Performs sequential decision-making in the Hopper environment
- Handles complex state transformations through normalization
- Generates action sequences based on historical trajectory data
- Optimized for medium-difficulty control scenarios
Frequently Asked Questions
Q: What makes this model unique?
This model uniquely combines Decision Transformer architecture with specific optimizations for the Gym Hopper environment, using carefully calibrated normalization coefficients for state processing.
Q: What are the recommended use cases?
The model is best suited for robotics control tasks within the Gym Hopper environment, particularly for scenarios requiring medium-complexity movement and control strategies.
