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Td3 HalfCheetah V3
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Td3 HalfCheetah V3

Developed by sb3
This is a TD3 reinforcement learning agent trained using the stable-baselines3 library, specifically designed for the HalfCheetah-v3 environment, achieving an average reward of 9709.01.
Physics Model#Continuous Action Control#High Reward Performance#Deep Reinforcement Learning
Downloads 23
Release Time : 6/2/2022

Model Overview

TD3 (Twin Delayed DDPG) is a deep reinforcement learning algorithm suitable for continuous action space control tasks. This model performs exceptionally well in the HalfCheetah-v3 environment, efficiently controlling a simulated cheetah robot's movement.

Model Features

High-Performance Control
Achieved an average reward of 9709.01 in the HalfCheetah-v3 environment, demonstrating outstanding performance.
Stable Training
Utilizes the TD3 algorithm, ensuring training stability through techniques like twin Q-networks and delayed policy updates.
Easy Integration
Based on the stable-baselines3 framework, it can be easily integrated with other RL tools and libraries.

Model Capabilities

Continuous Action Space Control
Robot Motion Control
Reinforcement Learning Task Solving

Use Cases

Robot Control
Cheetah Robot Motion Control
Controls a simulated cheetah robot for efficient movement
Average reward reaches 9709.01
Algorithm Research
Reinforcement Learning Algorithm Comparison
Serves as a benchmark model for comparing the performance of different reinforcement learning algorithms
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