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Decision Transformer Gym Hopper Medium
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Decision Transformer Gym Hopper Medium

Developed by edbeeching
This is a decision transformer model trained on medium-performance trajectories in the Gym Hopper environment, suitable for continuous control tasks.
Physics Model
Transformers
#Reinforcement Learning Control#Trajectory Decision Optimization#Continuous Action Space
Downloads 6,518
Release Time : 3/16/2022

Model Overview

The model is based on the Decision Transformer architecture and is specifically trained for continuous control tasks in the Gym Hopper environment, capable of generating corresponding action decisions based on environmental states.

Model Features

Trajectory-Based Decision Generation
The model generates decisions by learning from medium-performance trajectories, making it suitable for continuous control tasks.
State Normalization
Provides detailed normalization coefficients to ensure proper handling of input states.
Reinforcement Learning Application
Designed for reinforcement learning environments, particularly suitable for continuous control tasks like Gym Hopper.

Model Capabilities

Continuous Action Space Decision-Making
Reinforcement Learning Environment Control
Trajectory Learning

Use Cases

Robotics Control
Hopper Robot Motion Control
Controls the hopping and balancing of the Hopper robot
Achieves medium-level motion performance
Reinforcement Learning Research
Decision Transformer Algorithm Validation
Used to validate the performance of Decision Transformer in continuous control tasks
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