Event Tables for Efficient Experience Replay
- URL: http://arxiv.org/abs/2211.00576v2
- Date: Fri, 21 Apr 2023 11:10:16 GMT
- Title: Event Tables for Efficient Experience Replay
- Authors: Varun Kompella, Thomas J. Walsh, Samuel Barrett, Peter Wurman, Peter
Stone
- Abstract summary: Experience replay (ER) is a crucial component of many deep reinforcement learning (RL) systems.
Uniform sampling from an ER buffer can lead to slow convergence and unstable behaviors.
This paper introduces Stratified Sampling from Event Tables (SSET), which partitions an ER buffer into Event Tables.
- Score: 31.678826875509348
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Experience replay (ER) is a crucial component of many deep reinforcement
learning (RL) systems. However, uniform sampling from an ER buffer can lead to
slow convergence and unstable asymptotic behaviors. This paper introduces
Stratified Sampling from Event Tables (SSET), which partitions an ER buffer
into Event Tables, each capturing important subsequences of optimal behavior.
We prove a theoretical advantage over the traditional monolithic buffer
approach and combine SSET with an existing prioritized sampling strategy to
further improve learning speed and stability. Empirical results in challenging
MiniGrid domains, benchmark RL environments, and a high-fidelity car racing
simulator demonstrate the advantages and versatility of SSET over existing ER
buffer sampling approaches.
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