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Shivakanth Sujit

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Prioritizing Samples in Reinforcement Learning with Reducible Loss
Shivakanth Sujit
Somjit Nath
Pedro H.M. Braga
Samira Ebrahimi Kahou
Most reinforcement learning algorithms take advantage of an experience replay buffer to repeatedly train on samples the agent has observed i… (see more)n the past. Not all samples carry the same amount of significance and simply assigning equal importance to each of the samples is a naïve strategy. In this paper, we propose a method to prioritize samples based on how much we can learn from a sample. We define the learn-ability of a sample as the steady decrease of the training loss associated with this sample over time. We develop an algorithm to prioritize samples with high learn-ability, while assigning lower priority to those that are hard-to-learn, typically caused by noise or stochasticity. We empirically show that our method is more robust than random sampling and also better than just prioritizing with respect to the training loss, i.e. the temporal difference loss, which is used in prioritized experience replay.
2023-09-20
NeurIPS.cc/2023/Conference (poster)
doi.org
openreview.net