DQN - BreakoutNoFrameskip-v4 Open-source Model - Help You Enjoy Playing the Atari Game BreakoutNoFrameskip-v4

Dqn BreakoutNoFrameskip V4

Developed by sb3

This is a deep reinforcement learning model based on the DQN algorithm, specifically designed for the Atari game environment BreakoutNoFrameskip-v4.

Downloads 20

Release Time : 6/2/2022

Model Overview

The model is trained using the DQN algorithm from the stable-baselines3 library and can achieve high average scores in the Breakout game.

Frame stacking processing

Uses 4-frame stacking technique to capture game dynamics information

Efficient exploration strategy

Employs epsilon-greedy exploration strategy with exploration rate decaying from initial value to 0.01

Optimized memory usage

Enabled memory optimization options to improve training efficiency

Atari game control

Reinforcement learning decision-making

Pixel-level input processing

Game AI

Breakout game AI

Automatically plays Atari Breakout game

Average score 359±44.32

Reinforcement learning research

DQN algorithm benchmark

Serves as a benchmark model for deep Q-learning algorithms

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