Exploration by Maximizing Rényi Entropy for Reward-Free RL Framework
This work addresses exploration inefficiencies in meta RL settings where multiple reward functions are involved, offering a practical solution for complex environments.
The paper tackles the challenge of exploration in reinforcement learning by proposing a reward-free framework that separates exploration from exploitation, using Renyi entropy maximization to encourage exploration of hard-to-reach state-action pairs, resulting in effective and sample-efficient policies for arbitrary reward functions.
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 a dataset of transitions by executing the policy. In the planning phase, the agent computes a good policy for any reward function based on the dataset without further interacting with the environment. This framework is suitable for the meta RL setting where there are many reward functions of interest. In the exploration phase, we propose to maximize the Renyi entropy over the state-action space and justify this objective theoretically. The success of using Renyi entropy as the objective results from its encouragement to explore the hard-to-reach state-actions. We further deduce a policy gradient formulation for this objective and design a practical exploration algorithm that can deal with complex environments. In the planning phase, we solve for good policies given arbitrary reward functions using a batch RL algorithm. Empirically, we show that our exploration algorithm is effective and sample efficient, and results in superior policies for arbitrary reward functions in the planning phase.