Deep Reinforcement Learning Using a Low-Dimensional Observation Filter for Visual Complex Video Game Playing

Deep Reinforcement Learning (DRL) has produced great achievements since it was proposed, including the possibility of processing raw vision input data. However, training an agent to perform tasks based on image feedback remains a challenge. It requires the processing of large amounts of data from hi...

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Bibliographic Details
Published in:arXiv.org 2022-04
Main Authors: Kich, Victor Augusto, Junior Costa de Jesus, Grando, Ricardo Bedin, Kolling, Alisson Henrique, Heisler, Gabriel Vinícius, Rodrigo da Silva Guerra
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Complexity
Computer & video games
Deep learning
Feature extraction
Machine learning
Neon
Neural networks
Visual observation
Online Access:Get full text
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Summary:Deep Reinforcement Learning (DRL) has produced great achievements since it was proposed, including the possibility of processing raw vision input data. However, training an agent to perform tasks based on image feedback remains a challenge. It requires the processing of large amounts of data from high-dimensional observation spaces, frame by frame, and the agent's actions are computed according to deep neural network policies, end-to-end. Image pre-processing is an effective way of reducing these high dimensional spaces, eliminating unnecessary information present in the scene, supporting the extraction of features and their representations in the agent's neural network. Modern video-games are examples of this type of challenge for DRL algorithms because of their visual complexity. In this paper, we propose a low-dimensional observation filter that allows a deep Q-network agent to successfully play in a visually complex and modern video-game, called Neon Drive.
ISSN:2331-8422