ORAL: Adaptive Gap Increasing for Advantage Learning via Occam's Razor Principle.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning

Background:

• Advantage learning (AL) operators in reinforcement learning (RL) are more robust to Q-function estimation errors than Bellman optimality operators.
• However, AL's robustness and larger action gaps can lead to slower value function convergence due to worse performance loss bounds.

Purpose of the Study:

• To address the slower convergence issue in AL, this study introduces Occam's Razor-based AL (ORAL).
• ORAL aims to improve the convergence rate of value functions in RL by adaptively managing the action gap.

Main Methods:

• A novel method, ORAL, is proposed, applying Occam's Razor principle to increase action gaps only when necessary.
• ORAL adaptively increases the action gap based on the proximity of state-action Q-values to optimal values.
• Both a nonsmooth clipping function implementation and a smooth version for stable learning are presented.

Main Results:

• Theoretical analysis confirms ORAL's feasibility in balancing action gap increases with fast convergence.
• Empirical results demonstrate significant performance improvements across various benchmarks.
• The ORAL methods are shown to be pluggable into existing AL operators and extendable to continuous-control tasks.

Conclusions:

• ORAL effectively mitigates the slower convergence associated with traditional AL methods.
• The adaptive approach of ORAL enhances the practical applicability of AL in complex RL scenarios.
• ORAL represents a significant advancement in improving the efficiency and performance of reinforcement learning algorithms.