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Ppo BipedalWalker V3
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Ppo BipedalWalker V3

Developed by sb3
This is a PPO agent model trained using the stable-baselines3 library, specifically designed for reinforcement learning tasks in the BipedalWalker-v3 environment.
Protein Model#Bipedal Robot Control#Reinforcement Learning Training#High-Dimensional Action Space
Downloads 22
Release Time : 6/2/2022

Model Overview

The model is based on the PPO (Proximal Policy Optimization) algorithm, used to train a bipedal walking robot to achieve stable walking in the BipedalWalker-v3 environment.

Model Features

High-Performance Reinforcement Learning
Achieved an average reward value of 288.30 in the BipedalWalker-v3 environment
Parallel Training
Trained using 32 parallel environments to improve training efficiency
Parameter Optimization
Carefully tuned hyperparameters including learning rate, batch size, etc.

Model Capabilities

Bipedal Walking Control
Reinforcement Learning Training
Environment Interaction

Use Cases

Robot Control
Bipedal Walking Robot Training
Train a bipedal robot to achieve stable walking
Average reward reached 288.30 ± 2.23
Reinforcement Learning Research
PPO Algorithm Performance Verification
Verify the performance of PPO algorithm in continuous control tasks
Performed well in the BipedalWalker-v3 environment
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