Entropy Reward

Entropy reward methods aim to improve the performance and robustness of reinforcement learning (RL) and generative models by incorporating entropy as a reward signal or regularizer. Current research focuses on addressing challenges like "reward collapse" in diffusion models and promoting predictable, interpretable behavior in RL agents through entropy rate minimization, often employing techniques like soft actor-critic (SAC) and population-based training. These approaches are significant because they enhance exploration, mitigate overfitting to imperfect reward functions, and lead to more diverse and reliable model outputs, with applications ranging from image generation to human-AI collaboration.