The entropy term in policy optimization encourages exploration by preventing the policy from becoming too deterministic too quickly. In reinforcement learning, policies often use probability distributions to select actions. The entropy term, added to the loss function, measures the “randomness” of these distributions. A higher entropy value means the policy is more uncertain and explores more actions, while lower entropy implies confidence in specific choices. By including this term with a tunable coefficient (e.g., in algorithms like A2C or PPO), the optimization process balances exploiting known good actions with exploring new ones. For example, in a grid-world task, a policy without entropy might fixate on moving right even if a better path exists left, while entropy ensures it occasionally tests alternatives.
The entropy term also mitigates premature convergence to suboptimal policies. Without entropy, a policy might quickly assign near-zero probabilities to actions that initially seem poor but could yield better long-term rewards. For instance, in a game where an agent must jump over obstacles, a deterministic policy might repeatedly fail by jumping too early. With entropy, the policy retains some probability of jumping later, allowing it to discover the correct timing. This is especially critical in environments with sparse rewards, where early mistakes might discourage exploration. The entropy term acts as a regularizer, keeping the policy “open” to alternatives until it gathers enough data to make informed decisions.
Practically, the entropy coefficient determines how much exploration is incentivized. Developers often adjust this hyperparameter based on the problem: complex environments with many unknowns require higher coefficients to sustain exploration, while simpler tasks might need lower values to prioritize exploitation. For example, in robotic control tasks with continuous action spaces, a higher entropy term helps the policy maintain diverse motor commands until it identifies the most efficient movements. However, too much entropy can slow convergence by over-prioritizing random actions. Algorithms like Soft Actor-Critic (SAC) automate entropy tuning by treating it as part of the optimization objective, dynamically adjusting the balance between exploration and exploitation as training progresses.