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Dqn Mountaincar V0 Zoo
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Dqn Mountaincar V0 Zoo

Developed by Galeros
This is a reinforcement learning agent based on Deep Q-Network (DQN), specifically designed to solve tasks in the MountainCar-v0 environment.
Physics Model#Deep Q-Learning#MountainCar Control#Reinforcement Learning Training
Downloads 16
Release Time : 6/11/2022

Model Overview

This model is trained using the stable-baselines3 library and can learn effective control strategies in the MountainCar-v0 environment to successfully drive the car to the mountaintop.

Model Features

Deep Reinforcement Learning
Uses deep neural networks as function approximators, capable of handling high-dimensional state spaces.
Stable Training
Ensures training stability through techniques like experience replay and target networks.
Efficient Learning
Capable of learning effective control strategies within a limited time frame.

Model Capabilities

Solving continuous control problems
Learning optimal strategies
Adapting to dynamic environments

Use Cases

Educational Demonstration
Reinforcement Learning Teaching
Used to demonstrate the application of deep reinforcement learning algorithms in real-world control problems.
Students can intuitively understand how the DQN algorithm works.
Algorithm Research
Reinforcement Learning Algorithm Comparison
Serves as a benchmark model for comparing the performance of different reinforcement learning algorithms.
Average reward reaches -105.00 +/- 3.46.
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