Application of Rainbow DQN and Curriculum Learning in Atari Breakout

Hengqian Wu

2024

Abstract

Reinforcement Learning has become a crucial area within artificial intelligence, particularly when it comes to applying these techniques in environments like video games, robotics, and autonomous systems. The Deep Q-Network, introduced by DeepMind in 2015, marked a significant advancement by enabling AI agents to play Atari games directly from raw pixel inputs. However, this network encounters issues with large state spaces and tends to overestimate action values, leading to inefficient learning and not-so-optimal performance. To overcome these limitations, Rainbow DQN integrates several enhancements, such as Double Q-learning, Prioritized Experience Replay, and Noisy Networks, which together greatly improve the algorithm's performance. Additionally, Curriculum Learning, which systematically escalates task difficulty, mimics the human learning process, and enhances the agent's efficiency. This paper delves into the combination of Rainbow DQN and Curriculum Learning within the context of Atari Breakout, offering a detailed look at how these techniques work together to boost both the agent's game score and learning speed. Experimental outcomes display that this method significantly improves the agent's game score, learning pace, and overall adaptability in complex scenarios.

Paper Citation

in Harvard Style

Wu H. (2024). Application of Rainbow DQN and Curriculum Learning in Atari Breakout. In Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM; ISBN 978-989-758-738-2, SciTePress, pages 163-166. DOI: 10.5220/0013245500004558

in Bibtex Style

@conference{mlscm24,
author={Hengqian Wu},
title={Application of Rainbow DQN and Curriculum Learning in Atari Breakout},
booktitle={Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM},
year={2024},
pages={163-166},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013245500004558},
isbn={978-989-758-738-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM
TI - Application of Rainbow DQN and Curriculum Learning in Atari Breakout
SN - 978-989-758-738-2
AU - Wu H.
PY - 2024
SP - 163
EP - 166
DO - 10.5220/0013245500004558
PB - SciTePress