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Dqn MountainCar V0
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Dqn MountainCar V0

Developed by sb3
This is a DQN agent model trained using stable-baselines3, specifically designed to solve reinforcement learning tasks in the MountainCar-v0 environment.
Molecular Model#Deep Q-Network#Reinforcement Learning Control#Continuous Action Space
Downloads 578
Release Time : 5/19/2022

Model Overview

This model is based on the Deep Q-Network (DQN) algorithm, designed to solve the classic MountainCar control problem, with the goal of making the car swing and climb to the top of the hill.

Model Features

Deep Reinforcement Learning-Based
Utilizes the Deep Q-Network (DQN) algorithm, combining deep neural networks and reinforcement learning techniques.
Optimized Hyperparameters
Carefully tuned hyperparameter combinations, including learning rate and exploration rate.
Stable Training Framework
Built on the stable-baselines3 and RL Zoo training framework to ensure training stability.

Model Capabilities

Solving continuous control problems
Learning optimal policies
Adapting to the MountainCar environment

Use Cases

Educational Demonstration
Reinforcement Learning Teaching
Used to demonstrate the application of deep reinforcement learning algorithms in classic control problems.
Average reward reaches -103.40
Algorithm Research
DQN Algorithm Benchmarking
Serves as a performance benchmark for other reinforcement learning algorithms.
Provides comparable performance metrics
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