Exploration by Maximizing Renyi Entropy for Reward-Free RL Framework
Exploration by Maximizing Renyi Entropy for Reward-Free RL Framework
Exploration is essential for reinforcement learning (RL). To face the challenges of exploration, we consider a reward-free RL framework that completely separates exploration from exploitation and brings new challenges for exploration algorithms. In the exploration phase, the agent learns an exploratory policy by interacting with a reward-free environment and collects …