On The Presence of Double-Descent in Deep Reinforcement Learning

2025-11-10

AI Summary

Generalization in model-free deep reinforcement learning (DRL) remains challenging due to non-stationary environments and poorly understood capacity–performance relationships. Method: The authors systematically investigate the double descent (DD) phenomenon within the Actor-Critic framework, introducing policy entropy—a principled information-theoretic measure—to quantify model capacity and track training dynamics under controlled overparameterization. Contribution/Results: This work provides the first empirical validation of DD in model-free DRL: beyond the interpolation threshold, generalization error exhibits a second descent, coinciding with a sustained, significant reduction in policy entropy. This suggests that overparameterization induces implicit regularization, steering policies toward flatter, more robust minima. Beyond establishing DD as an empirically grounded phenomenon in DRL, the study proposes policy entropy as a mechanistic bridge for interpreting DD—offering a novel paradigm for designing agents with enhanced generalization and cross-task transferability.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Search and Optimization: Learning to Search
• Multiagent Systems: Multiagent Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

The double descent (DD) paradox, where over-parameterized models see generalization improve past the interpolation point, remains largely unexplored in the non-stationary domain of Deep Reinforcement Learning (DRL). We present preliminary evidence that DD exists in model-free DRL, investigating it systematically across varying model capacity using the Actor-Critic framework. We rely on an information-theoretic metric, Policy Entropy, to measure policy uncertainty throughout training. Preliminary results show a clear epoch-wise DD curve; the policy's entrance into the second descent region correlates with a sustained, significant reduction in Policy Entropy. This entropic decay suggests that over-parameterization acts as an implicit regularizer, guiding the policy towards robust, flatter minima in the loss landscape. These findings establish DD as a factor in DRL and provide an information-based mechanism for designing agents that are more general, transferable, and robust.

Problem

• Investigating double-descent phenomenon in deep reinforcement learning systems
• Analyzing policy entropy as information-theoretic metric for training uncertainty
• Exploring over-parameterization as implicit regularizer for robust policies

Innovation

• Using Actor-Critic framework with varying model capacity
• Applying Policy Entropy metric to measure policy uncertainty
• Over-parameterization acts as implicit regularizer for robust policies