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Ppo MountainCarContinuous V0
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Ppo MountainCarContinuous V0

Developed by sb3
This is a reinforcement learning agent based on the PPO algorithm, specifically trained for the MountainCarContinuous-v0 environment, capable of effectively solving the continuous control problem of the mountain car.
Physics Model#Continuous Action Control#Few-Shot Efficient Training#Custom Policy Optimization
Downloads 433
Release Time : 5/20/2022

Model Overview

This model is trained using the PPO algorithm from the stable-baselines3 library and is suitable for the MountainCarContinuous-v0 environment, learning how to control the mountain car to reach the summit.

Model Features

Efficient Training
Uses the PPO algorithm for training, achieving high average rewards with fewer training steps.
Stable Performance
The model exhibits stable performance with an average reward of 94.57±0.45.
Parameter Optimization
Uses the RL Zoo framework for hyperparameter optimization to ensure optimal model performance.

Model Capabilities

Continuous action space control
Reinforcement learning task solving
Environment interaction learning

Use Cases

Reinforcement Learning Research
Continuous Control Benchmark
Can serve as a benchmark model for comparative studies in continuous control tasks
Average reward 94.57±0.45
Teaching Demonstration
Reinforcement Learning Teaching Case
Used to demonstrate the application of the PPO algorithm in continuous control tasks
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