What is policy regularization?

Policy regularization is a technique used in reinforcement learning (RL) to prevent an agent’s decision-making strategy (its “policy”) from becoming too rigid or overfitting to specific scenarios. It works by adding constraints or penalties to the learning process, encouraging the policy to generalize better across diverse situations. For example, in algorithms like Proximal Policy Optimization (PPO), regularization might involve discouraging large updates to the policy or penalizing low entropy (uncertainty) in action choices. This helps balance the agent’s focus between exploiting known effective actions and exploring new possibilities, which is critical for robust performance.

The need for policy regularization arises because RL agents often optimize for immediate rewards, which can lead to unstable or brittle behaviors. Without regularization, a policy might overfit to the training environment’s quirks, such as exploiting a specific sequence of actions that only works under narrow conditions. For instance, an agent trained in a simulated environment with perfectly predictable physics might fail in the real world where noise and variability exist. Regularization methods like entropy bonuses (encouraging the policy to maintain diverse action probabilities) or weight decay (limiting the magnitude of neural network parameters) mitigate this by promoting simpler, more adaptable policies. This is analogous to how L1/L2 regularization prevents overfitting in supervised learning models.

Implementing policy regularization typically involves modifying the loss function used during training. For example, in PPO, the loss function combines the policy gradient objective with an entropy term and a penalty for large policy updates. A developer might add an entropy coefficient (e.g., beta=0.01 in PPO) to control the strength of the entropy bonus. Code-wise, this could look like loss = policy_loss - beta * entropy + L2_penalty, where L2_penalty discourages large weights in the neural network. Practical tuning of these coefficients is essential—too much regularization can stifle learning, while too little leads to instability. Frameworks like TensorFlow or PyTorch simplify this by allowing developers to directly incorporate these terms into their optimization loops, making policy regularization accessible even in complex RL setups.