Decision Transformer Gym Walker2d Expert
| Property | Value |
|---|---|
| Author | edbeeching |
| Research Paper | Decision Transformer Paper |
| Framework | PyTorch |
| Tags | Reinforcement Learning, Transformers, Decision Transformer |
What is decision-transformer-gym-walker2d-expert?
This is a specialized implementation of the Decision Transformer architecture trained specifically for the Gym Walker2d environment. The model leverages expert trajectories to learn optimal control policies for bipedal walking tasks, utilizing transformer-based sequence modeling for continuous control.
Implementation Details
The model implements sophisticated normalization coefficients with 17-dimensional state representations, including specific mean and standard deviation values for optimal performance. It's built using PyTorch and integrates deep reinforcement learning principles with transformer architecture.
- Specialized normalization coefficients for state representation
- 17-dimensional state space handling
- Expert trajectory-based training
- Continuous control optimization
Core Capabilities
- Bipedal locomotion control in the Walker2d environment
- Sequence-based decision making
- Expert-level performance replication
- Continuous action space handling
Frequently Asked Questions
Q: What makes this model unique?
This model uniquely combines transformer architecture with reinforcement learning for continuous control tasks, specifically optimized for the Walker2d environment using expert demonstrations.
Q: What are the recommended use cases?
The model is specifically designed for bipedal locomotion tasks in the Gym Walker2d environment, making it ideal for research in continuous control, robotics simulation, and reinforcement learning applications.
