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Dqn CartPole V1
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Dqn CartPole V1

Developed by sb3
This is a reinforcement learning model based on Deep Q-Network (DQN), specifically designed to solve the balancing pole problem in the CartPole-v1 environment.
Molecular Model#Reinforcement Learning Control#CartPole Balancing#Deep Q-Network
Downloads 35
Release Time : 6/2/2022

Model Overview

The model is implemented using the stable-baselines3 library and can stably maintain pole balance in the CartPole-v1 environment, achieving the maximum reward of 500 points.

Model Features

High-Performance Balance Control
Achieves a perfect average reward of 500 points in the CartPole-v1 environment
Optimized Hyperparameters
Uses carefully tuned hyperparameter combinations to ensure training efficiency and stability
Dual Network Architecture
Adopts the standard DQN implementation, including target network and experience replay mechanism

Model Capabilities

Reinforcement Learning Control
Continuous Action Space Handling
Balance Control

Use Cases

Educational Demonstration
Reinforcement Learning Teaching Example
Used to demonstrate the basic principles of reinforcement learning and the working mechanism of the DQN algorithm
Visually demonstrates how the agent learns to keep the pole balanced
Algorithm Benchmarking
Reinforcement Learning Algorithm Comparison
Serves as a performance benchmark for other reinforcement learning algorithms in the CartPole environment
Provides a perfect score of 500 points as a reference standard
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