On The Presence of Double-Descent in Deep Reinforcement Learning
This work addresses the double-descent paradox for researchers in deep reinforcement learning, though it is incremental as it extends known concepts to a new domain.
The paper investigates the double-descent phenomenon in deep reinforcement learning, finding preliminary evidence that over-parameterized models show improved generalization, with results indicating a clear epoch-wise double-descent curve and a correlation with reduced policy entropy.
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.