Data Augmentation through Expert-guided Symmetry Detection to Improve Performance in Offline Reinforcement Learning
This work addresses data scarcity in offline RL for non-deterministic environments, but it is incremental as it builds on prior methods for deterministic cases.
The paper tackles the problem of offline reinforcement learning by extending an expert-guided symmetry detection method to non-deterministic Markov Decision Processes, resulting in a performance improvement when applying optimized policies in real environments.
Offline estimation of the dynamical model of a Markov Decision Process (MDP) is a non-trivial task that greatly depends on the data available in the learning phase. Sometimes the dynamics of the model is invariant with respect to some transformations of the current state and action. Recent works showed that an expert-guided pipeline relying on Density Estimation methods as Deep Neural Network based Normalizing Flows effectively detects this structure in deterministic environments, both categorical and continuous-valued. The acquired knowledge can be exploited to augment the original data set, leading eventually to a reduction in the distributional shift between the true and the learned model. Such data augmentation technique can be exploited as a preliminary process to be executed before adopting an Offline Reinforcement Learning architecture, increasing its performance. In this work we extend the paradigm to also tackle non-deterministic MDPs, in particular, 1) we propose a detection threshold in categorical environments based on statistical distances, and 2) we show that the former results lead to a performance improvement when solving the learned MDP and then applying the optimized policy in the real environment.