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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.

Model Features

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

Model Capabilities

Atari game control
Reinforcement learning decision-making
Pixel-level input processing

Use Cases

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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