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Td3 Hopper V3
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Td3 Hopper V3

Developed by sb3
This is a TD3 agent model trained using the stable-baselines3 library, specifically designed for reinforcement learning tasks in the Hopper-v3 environment.
Physics Model#Reinforcement Learning Control#Robot Motion Control#Continuous Action Space
Downloads 30
Release Time : 6/2/2022

Model Overview

This model is trained using the Twin Delayed DDPG (TD3) algorithm and is suitable for reinforcement learning tasks in continuous action spaces, particularly excelling in the Hopper-v3 environment.

Model Features

High-Performance Control
Achieved an average reward of 3604.63 in the Hopper-v3 environment, demonstrating excellent performance.
Stable Training
Utilizes the TD3 algorithm, effectively addressing the overestimation issue in DDPG algorithms, resulting in more stable training.
Easy Integration
Seamlessly integrates with the stable-baselines3 and RL Zoo frameworks, making it easy to use and extend.

Model Capabilities

Continuous Action Space Control
Reinforcement Learning Task Execution
Robot Motion Control

Use Cases

Robot Control
Single-Leg Robot Hopping Control
Controls a single-leg robot in a simulated environment to perform hopping and balancing tasks.
Achieved an average reward of 3604.63
Algorithm Research
Reinforcement Learning Algorithm Comparison
Serves as a benchmark model for comparing the performance of different reinforcement learning algorithms.
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