Adaptive Regularization of Representation Rank as an Implicit Constraint of Bellman Equation

TL;DR

This paper introduces BEER, a novel regularizer based on the Bellman equation, which adaptively controls the representation rank in deep reinforcement learning, leading to improved performance and better Q-value approximation in complex tasks.

Contribution

The paper proposes BEER, a new regularizer that adaptively manages representation rank using the Bellman equation, enhancing DRL performance and stability.

Findings

BEER outperforms baselines on 12 DeepMind control tasks.

Adaptive regularization improves Q-value approximation.

The method scales effectively to complex continuous control tasks.

Abstract

Representation rank is an important concept for understanding the role of Neural Networks (NNs) in Deep Reinforcement learning (DRL), which measures the expressive capacity of value networks. Existing studies focus on unboundedly maximizing this rank; nevertheless, that approach would introduce overly complex models in the learning, thus undermining performance. Hence, fine-tuning representation rank presents a challenging and crucial optimization problem. To address this issue, we find a guiding principle for adaptive control of the representation rank. We employ the Bellman equation as a theoretical foundation and derive an upper bound on the cosine similarity of consecutive state-action pairs representations of value networks. We then leverage this upper bound to propose a novel regularizer, namely BEllman Equation-based automatic rank Regularizer (BEER). This regularizer adaptively regularizes the representation rank, thus improving the DRL agent's performance. We first validate the effectiveness of automatic control of rank on illustrative experiments. Then, we scale up BEER to complex continuous control tasks by combining it with the deterministic policy gradient method. Among 12 challenging DeepMind control tasks, BEER outperforms the baselines by a large margin. Besides, BEER demonstrates significant advantages in Q-value approximation. Our code is available at https://github.com/sweetice/BEER-ICLR2024.