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$\beta$-DQN: Improving Deep Q-Learning By Evolving the Behavior

reinforcement-learning › robotics-rl

📄 Abstract

Abstract: While many sophisticated exploration methods have been proposed, their lack of generality and high computational cost often lead researchers to favor simpler methods like $\epsilon$-greedy. Motivated by this, we introduce $\beta$-DQN, a simple and efficient exploration method that augments the standard DQN with a behavior function $\beta$. This function estimates the probability that each action has been taken at each state. By leveraging $\beta$, we generate a population of diverse policies that balance exploration between state-action coverage and overestimation bias correction. An adaptive meta-controller is designed to select an effective policy for each episode, enabling flexible and explainable exploration. $\beta$-DQN is straightforward to implement and adds minimal computational overhead to the standard DQN. Experiments on both simple and challenging exploration domains show that $\beta$-DQN outperforms existing baseline methods across a wide range of tasks, providing an effective solution for improving exploration in deep reinforcement learning.

Authors (6)

Hongming Zhang

Fengshuo Bai

Chenjun Xiao

Chao Gao

Bo Xu

Martin Müller

Submitted

January 1, 2025

arXiv Category

cs.LG

arXiv PDF

Key Contributions

This paper introduces $\beta$-DQN, a novel and efficient exploration method for Deep Q-Learning that augments standard DQN with a behavior function ($eta$) to estimate action probabilities. This allows $\beta$-DQN to generate diverse policies balancing exploration and bias correction, adaptively selecting the best policy per episode via a meta-controller, and achieving superior performance with minimal computational overhead.

Business Value

Enables more efficient training of RL agents for complex tasks like robotics control or game playing, reducing development time and improving performance.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

Highly feasible, as it's a simple augmentation to standard DQN with minimal computational overhead, making it easy to integrate into existing RL pipelines.

Limitations Addressed

The generality and high computational cost of many sophisticated exploration methods, often leading researchers to favor simpler, less effective methods like $\epsilon$-greedy.

Performance Gains

Outperforms existing baseline methods across a wide range of tasks, providing effective and explainable exploration.

Technical Tags

Deep Q-Learningexploration strategiesbehavior functionstate-action coverageoverestimation biasadaptive meta-controllerpolicy selectionreinforcement learningepsilon-greedycomputational overhead

Research Topics

Exploration in Reinforcement LearningDeep Reinforcement LearningPolicy OptimizationRL Algorithm DesignRobotics

Methods & Architectures

Deep Q-Learning (DQN)Behavior Function (beta)Adaptive Meta-ControllerPolicy Ensemble Deep Q-Networks (DQN)

Applications & Tasks

Robotics Game Playing Autonomous Systems Reinforcement Learning Exploration vs. ExploitationImproving RL sample efficiencyHandling complex state-action spaces Efficient exploration in RLLearning optimal policiesRobotic control

Related Fields

Reinforcement LearningDeep LearningRoboticsArtificial IntelligenceControl Theory

Keywords

Deep Q-Learningreinforcement learningexplorationepsilon-greedybehavior functionpolicy optimizationmeta-controllerroboticsgame playingsample efficiencyoverestimation biasstate-action coverageadaptive control

Academic Context

#Exploration in Reinforcement Learning#Deep Reinforcement Learning#Policy Optimization#RL Algorithm Design#Robotics

Commercial Potential

Potential Products

More capable robotic agentsAdvanced game-playing AIOptimized control systems

Target Industries

RoboticsGamingAutonomous VehiclesLogisticsManufacturing

Use Case Examples

Training robots for complex manipulation tasksDeveloping AI agents for strategy gamesOptimizing resource allocation in dynamic environments

Competitive Edge

Provides a more effective and computationally efficient exploration strategy compared to simpler methods like $\epsilon$-greedy and potentially more complex, costly alternatives.

Resource Requirements

Compute Needs

Similar to standard DQN training, requiring GPU resources for deep network training. The behavior function and meta-controller add minimal overhead.

Data Requirements

Requires environments suitable for reinforcement learning, such as simulated robotics tasks or games.

Deployment Constraints

Performance is dependent on the quality of the behavior function and the meta-controller's ability to adapt. Generalization to highly novel environments might still be a challenge.

Scalability

Builds upon DQN, which is generally scalable. The added components are designed to be efficient.

Production Readiness

Maturity Level

Research

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