Title: Augmentations in Offline Reinforcement Learning for Active Positioning
Primary area: reinforcement learning
Keywords: Offline Reinforcement Learning, Reinforcement Learning, Active Position, Off-Policy Learning, Value Function Geometry

Abstract: We propose a method for data augmentation in offline reinforcement learning applied to active positioning problems. 
The approach enables the training of off-policy models from a limited number of trajectories generated by a suboptimal logging policy.  
Our method is a trajectory-based augmentation technique that exploits task structure and quantify the effect of admissible perturbations on the data using the geometric
interplay of properties of the reward, the value function, and the logging policy.
Moreover, we show that by training an off-policy model with our augmentation while collecting data, the suboptimal logging policy can be supported 
during collection, leading to higher data quality and improved offline reinforcement learning performance.
We provide theoretical justification for these strategies and validate them empirically across positioning tasks of varying dimensionality and under partial observability.

TL;DR: We introduce a trajectory-based data augmentation method that improves offline reinforcement learning in active positioning tasks by leveraging task structure and geometric properties of rewards, values, and logging policies.