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

• URL: http://arxiv.org/abs/2210.13545v2
• Date: Mon, 17 Apr 2023 07:11:03 GMT
• Title: MEET: A Monte Carlo Exploration-Exploitation Trade-off for Buffer Sampling
• Authors: Julius Ott, Lorenzo Servadei, Jose Arjona-Medina, Enrico Rinaldi, Gianfranco Mauro, Daniela S\'anchez Lopera, Michael Stephan, Thomas Stadelmayer, Avik Santra, Robert Wille
• Abstract summary: State-of-the-art sampling strategies for the experience replay buffer improve the performance of the Reinforcement Learning agent. They do not incorporate uncertainty in the Q-Value estimation. This paper proposes a new sampling strategy that leverages the exploration-exploitation trade-off.
• Score: 2.501153467354696
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data selection is essential for any data-based optimization technique, such as Reinforcement Learning. State-of-the-art sampling strategies for the experience replay buffer improve the performance of the Reinforcement Learning agent. However, they do not incorporate uncertainty in the Q-Value estimation. Consequently, they cannot adapt the sampling strategies, including exploration and exploitation of transitions, to the complexity of the task. To address this, this paper proposes a new sampling strategy that leverages the exploration-exploitation trade-off. This is enabled by the uncertainty estimation of the Q-Value function, which guides the sampling to explore more significant transitions and, thus, learn a more efficient policy. Experiments on classical control environments demonstrate stable results across various environments. They show that the proposed method outperforms state-of-the-art sampling strategies for dense rewards w.r.t. convergence and peak performance by 26% on average.

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