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Mind the GAP! The Challenges of Scale in Pixel-based Deep Reinforcement Learning

reinforcement-learning › game-playing

📄 Abstract

Abstract: Scaling deep reinforcement learning in pixel-based environments presents a significant challenge, often resulting in diminished performance. While recent works have proposed algorithmic and architectural approaches to address this, the underlying cause of the performance drop remains unclear. In this paper, we identify the connection between the output of the encoder (a stack of convolutional layers) and the ensuing dense layers as the main underlying factor limiting scaling capabilities; we denote this connection as the bottleneck, and we demonstrate that previous approaches implicitly target this bottleneck. As a result of our analyses, we present global average pooling as a simple yet effective way of targeting the bottleneck, thereby avoiding the complexity of earlier approaches.

Authors (2)

Ghada Sokar

Pablo Samuel Castro

Submitted

May 23, 2025

arXiv Category

cs.LG

arXiv PDF

Key Contributions

This paper identifies the bottleneck between the encoder's output and dense layers as the primary cause of performance degradation in scaled pixel-based deep reinforcement learning. It proposes global average pooling as a simple and effective solution to target this bottleneck, avoiding the complexity of previous methods.

Business Value

Improved performance and scalability in RL applications can lead to more capable AI agents in areas like gaming, simulation, and robotics, reducing development time and costs.

Paper Metadata

Innovation Type

Algorithmic Improvement

Deployment Feasibility

High, as the proposed solution (global average pooling) is a simple architectural change that can be readily integrated into existing RL frameworks.

Limitations Addressed

Addresses the performance drop and scaling limitations in pixel-based deep reinforcement learning that were not fully understood or effectively solved by prior algorithmic and architectural approaches.

Technical Tags

deep reinforcement learningpixel-based environmentsconvolutional layersdense layersglobal average poolingencoder-decoderbottleneck analysisscaling limitations

Research Topics

Reinforcement LearningDeep LearningModel ScalingPerception in RLArchitectural Analysis

Methods & Architectures

Deep Reinforcement LearningConvolutional Neural NetworksGlobal Average Pooling CNN EncoderDense Layers

Applications & Tasks

Video Games Simulated Environments Performance DegradationScalability Issues Pixel-based RLAgent Training

Related Fields

Machine LearningComputer VisionArtificial Intelligence

Keywords

Deep Reinforcement LearningPixel-based RLScalingPerformanceBottleneckGlobal Average PoolingCNNEncoderDense LayersArchitectural DesignRL Challenges

Academic Context

#Reinforcement Learning#Deep Learning#Model Scaling#Perception in RL#Architectural Analysis

Commercial Potential

Potential Products

More efficient RL training platformsAdvanced game AI

Target Industries

GamingSimulationRobotics

Use Case Examples

Training agents for complex video gamesDeveloping more robust simulated environments

Competitive Edge

Offers a simpler and more effective solution compared to complex algorithmic or architectural modifications previously used to address scaling issues in pixel-based RL.

Resource Requirements

Compute Needs

Likely high, typical for deep reinforcement learning on pixel inputs.

Data Requirements

Requires pixel-based environments for training and evaluation.

Deployment Constraints

Performance gains might be task-specific; requires careful tuning.

Scalability

The paper directly addresses scalability limitations in RL.

Production Readiness

Maturity Level

Research

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