Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration

2 Jun 2020 Seungyul Han Youngchul Sung

Policy entropy regularization is commonly used for better exploration in deep reinforcement learning (RL). However, policy entropy regularization is sample-inefficient in off-policy learning since it does not take the distribution of previous samples stored in the replay buffer into account... (read more)

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Methods used in the Paper

Hyperparameter Search
Entropy Regularization