Revisiting the Causal Mechanisms Behind Policy Gradients

This article explores the critical concepts behind policy gradient methods in Reinforcement Learning, highlighting the role of value function approximation and the importance of understanding implicit biases. It also discusses the overlooked significance of information theory in policy convergence.

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Policy gradient methods in Reinforcement Learning directly optimize a parameterized policy function to maximize expected rewards, guiding an agent toward optimal behavior. However, these methods are susceptible to high variance, which can impede learning efficiency and lead to slow convergence. To mitigate these issues, techniques like baselines and value function approximation are employed. Function approximation is a cornerstone of modern RL, but it introduces a subtle yet significant factor: implicit bias. This refers to the inherent preferences or tendencies embedded within the approximator's architecture or optimization process, which can profoundly influence the characteristics of the learned policy. Understanding these implicit biases is crucial for improving the effectiveness of RL agents. Information theory offers a powerful lens for understanding and enhancing policy convergence in RL. Principles like entropy regularization encourage policies to maintain a degree of stochasticity, promoting broader exploration of the environment. Examples include Soft Actor-Critic (SAC) and Soft Q-learning, which use entropy regularization to foster exploratory behavior. Beyond exploration, information-theoretic measures like mutual information and Kullback-Leibler (KL) divergence regularization play vital roles in stabilizing learning and facilitating knowledge sharing.