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Data selection is essential for any data-based optimization technique, suchas Reinforcement Learning. State-of-the-art sampling strategies for theexperience replay buffer improve the performance of the Reinforcement Learningagent. However, they do not incorporate uncertainty in the Q-Value estimation.Consequently, they cannot adapt the sampling strategies, including explorationand exploitation of transitions, to the complexity of the task. To addressthis, this paper proposes a new sampling strategy that leverages theexploration-exploitation trade-off. This is enabled by the uncertaintyestimation of the Q-Value function, which guides the sampling to explore moresignificant transitions and, thus, learn a more efficient policy. Experimentson classical control environments demonstrate stable results across variousenvironments. They show that the proposed method outperforms state-of-the-artsampling strategies for dense rewards w.r.t. convergence and peak performanceby 26% on average.

MEET: A Monte Carlo Exploration-Exploitation Trade-off for Buffer Sampling