Prioritizing Samples in Reinforcement Learning with Reducible Loss
This work addresses sample efficiency in reinforcement learning for practitioners, but it is incremental as it builds on existing prioritization methods.
The paper tackles the problem of inefficient sample prioritization in reinforcement learning by proposing a method that prioritizes samples based on their learn-ability, defined as steady loss decrease over time. Empirically, it shows improved robustness over random sampling and outperforms prioritized experience replay using temporal difference loss.
Most reinforcement learning algorithms take advantage of an experience replay buffer to repeatedly train on samples the agent has observed in 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.