decision-transformers

This survey examines in-context reinforcement learning (ICRL) under non-stationary environments, where a pretrained decision model adapts through accumulated context without parameter updates. It organizes the literature around what changes, how it unfolds, and how observable it is, and identifies research gaps such as stale-context stress tests and adaptive forgetting.