Dataset distillation for offline reinforcement learning
Keywords: Offline RL, Data Distillation
TL;DR: We use data distillation to train and distill a better and smaller dataset which can be used for training a policy model.
Abstract: Offline reinforcement learning often requires a quality dataset that we can train a policy on. However, in many situations, it is not possible to get such a dataset, nor is it easy to train a policy to perform well in the actual environment given the offline data. We propose using data distillation to train and distill a better dataset which can then be used for training a better policy model. We show that our method is able to synthesize a dataset where a model trained on it achieves similar performance to a model trained on the full dataset or a model trained using percentile behavioral cloning.
Submission Number: 37
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