AIbase
EN
Home
Td3 MountainCarContinuous V0
T

Td3 MountainCarContinuous V0

Developed by sb3
A TD3 reinforcement learning agent trained based on the stable-baselines3 library, specifically designed for the MountainCarContinuous-v0 environment.
Physics Model#Continuous Action Control#Deep Reinforcement Learning#MountainCar Environment
Downloads 203
Release Time : 6/2/2022

Model Overview

This model is trained using the Twin Delayed DDPG (TD3) algorithm to solve the continuous action space MountainCar task, with the goal of efficiently reaching the mountaintop.

Model Features

Efficient Continuous Control
Utilizes the TD3 algorithm, particularly suitable for handling control problems in continuous action spaces.
Stable Training
Enhances training stability through techniques like dual Q-networks and delayed policy updates.
Integrated Noise Mechanism
Uses Ornstein-Uhlenbeck noise strategy to improve exploration capabilities.

Model Capabilities

Continuous Action Space Control
Reinforcement Learning Task Solving
Environment Interaction Learning

Use Cases

Classic Control Problems
MountainCar Continuous Control
Controls the car to reach the mountaintop in a continuous action space.
Average reward reaches 93.46
Reinforcement Learning Research
Algorithm Benchmarking
Serves as a performance benchmark for the TD3 algorithm in continuous control tasks.
Featured Recommended AI Models
AIbase
Empowering the Future, Your AI Solution Knowledge Base
English简体中文繁體中文にほんご
© 2025AIbase