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

TL;DR

This paper introduces a low-dimensional observation filter that enhances deep reinforcement learning agents' ability to play complex visual video games by reducing input complexity and focusing on essential features.

Contribution

The paper proposes a novel low-dimensional observation filter that improves DRL performance in complex visual environments like modern video games.

Findings

The filter enables successful gameplay of Neon Drive using DRL.

It reduces computational complexity and improves learning efficiency.

The approach outperforms baseline methods in visual complexity handling.

Abstract

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.