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Ppo CartPole V1

Developed by sb3
This is a reinforcement learning model based on the PPO algorithm, specifically designed to solve the balancing problem in the CartPole-v1 environment.
Downloads 449
Release Time : 5/19/2022

Model Overview

The model is trained using the Proximal Policy Optimization (PPO) algorithm and can stably maintain pole balance in the CartPole-v1 environment, achieving the maximum reward of 500 points.

Model Features

High-performance PPO Algorithm
Utilizes the PPO algorithm for stable training and efficient learning
Multi-environment Parallel Training
Supports parallel training across 8 environments to improve training efficiency
Optimized Hyperparameters
Uses optimized hyperparameter configurations to ensure peak performance

Model Capabilities

CartPole Balancing Control
Reinforcement Learning Task Solving
Real-time Decision Making

Use Cases

Educational Demonstration
Reinforcement Learning Teaching Example
Serves as a classic case for introductory reinforcement learning education
Helps students understand the basic principles of reinforcement learning
Algorithm Research
PPO Algorithm Performance Research
Used to study the performance of the PPO algorithm in different environments
Provides benchmark performance references
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