PPO - CartPole-v1 Open Source Agent Model - Free Deployment to Solve Reinforcement Learning Tasks

Ppo CartPole V1

Developed by somya17ban

This is a PPO agent model trained using the stable-baselines3 library, specifically designed to solve the CartPole-v1 reinforcement learning task.

Downloads 14

Release Time : 5/11/2025

Model Overview

This model is based on the PPO (Proximal Policy Optimization) algorithm for controlling the pole balancing problem in the CartPole-v1 environment.

Efficient reinforcement learning

Uses PPO algorithm for efficient policy optimization, suitable for continuous action space problems

Stable training

PPO algorithm ensures training stability by limiting policy update magnitude

CartPole-v1 environment adaptation

Specifically optimized for CartPole-v1 environment, capable of achieving maximum rewards

Reinforcement learning control

Pole balance control

Continuous action space decision making

Educational demonstration

Reinforcement learning teaching

Used to demonstrate the application of reinforcement learning algorithms in classic control problems

Can stably maintain pole balance and achieve maximum rewards

Algorithm research

PPO algorithm benchmarking

Serves as a benchmark reference for PPO algorithm performance

Average reward reaches 500.00 +/- 0.00

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