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Dqn Mountaincar V0
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Dqn Mountaincar V0

Developed by Galeros
This is a reinforcement learning agent based on Deep Q-Network (DQN), specifically trained to solve control problems in the MountainCar-v0 environment.
Physics Model#Deep Q-Learning#MountainCar Control#Reinforcement Learning Training
Downloads 18
Release Time : 6/7/2022

Model Overview

This model is implemented using the stable-baselines3 library and trained through deep reinforcement learning. It can learn effective control strategies in the MountainCar-v0 environment to successfully drive the car to the top of the mountain.

Model Features

Deep Q-Learning
Uses deep neural networks to approximate the Q-value function, capable of handling high-dimensional state spaces.
Stable Training
Ensures training stability through techniques such as experience replay and target networks.
Environment Adaptation
Capable of learning effective control strategies in the MountainCar environment.

Model Capabilities

Reinforcement Learning Control
Policy Optimization
Environment Interaction

Use Cases

Educational Demonstration
Reinforcement Learning Teaching
Used to demonstrate the application of deep reinforcement learning algorithms in real-world control problems.
Average reward reaches -101.40 ± 9.64
Algorithm Research
DQN Algorithm Improvement
Serves as a baseline model for testing new reinforcement learning algorithm improvements.
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