entropyregularized
Entropyregularized refers to the practice of augmenting an optimization objective with an entropy term to promote higher-entropy, more diverse solutions. The approach is used across machine learning, reinforcement learning, and optimization to encourage exploration, avoid premature convergence, and yield robust, stable policies or models. The core idea is that, by penalizing low-entropy (overly deterministic) distributions, the solution remains flexible and less prone to overfitting.
Mathematically, entropy regularization typically adds a term proportional to the entropy of a probability distribution to
Common applications include entropy-regularized reinforcement learning, where the policy is optimized to maximize expected return plus
Choosing the regularization strength lambda is a practical concern, balancing exploration and convergence speed. If lambda