Entropy regularization improves exploration in reinforcement learning by encouraging the policy to maintain a balanced distribution over actions, preventing it from becoming too deterministic too quickly. In policy-based methods like Proximal Policy Optimization (PPO) or Soft Actor-Critic (SAC), the policy is a neural network that outputs probabilities for selecting actions. Without regularization, the policy might prematurely converge to a small set of high-reward actions, ignoring potentially better alternatives. Entropy regularization adds a term to the loss function that penalizes low entropy (i.e., high certainty), effectively pushing the policy to explore actions more evenly. This keeps the agent from getting stuck in suboptimal strategies early in training.
For example, consider a scenario where an agent navigates a maze with two paths: a known short path with a small reward and an unexplored longer path with a larger reward. Without entropy regularization, the agent might exploit the short path exclusively. With entropy regularization, the policy is incentivized to assign non-trivial probabilities to both paths, even if the short path initially appears better. Over time, this increases the chance of discovering the higher-reward path. In practice, the entropy term is calculated as the negative sum of policy probabilities multiplied by their log probabilities, scaled by a coefficient (e.g., 0.01 in PPO). This coefficient controls the trade-off between exploration (higher values) and exploitation (lower values).
From a developer’s perspective, entropy regularization simplifies exploration management compared to alternatives like epsilon-greedy or noisy networks. Instead of manually tuning exploration schedules, the entropy term automatically adapts based on the policy’s uncertainty. For instance, in the SAC algorithm, maximizing entropy is explicitly part of the objective, leading to more robust exploration in continuous action spaces. However, overusing entropy regularization can slow convergence, as the agent might prioritize randomness over learning. Developers often adjust the entropy coefficient during training—starting with higher values to encourage exploration and gradually reducing it to refine the policy. This approach balances efficient learning with thorough exploration, making it a practical tool for complex environments.