In-Context Reinforcement Learning Under Non-Stationarity: How Agents Adapt from Context Alone
In-Context Reinforcement Learning Under Non-Stationarity: How Agents Adapt from Context Alone A recent survey, In-Context Reinforcement Learning under Non-Stationarity , frames a growing line of work around in-context reinforcement learning (ICRL) and explains why it is drawing renewed attention now. The survey points to decision-pretrained transformers, algorithm distillation, long-context meta-RL, and retrieval-augmented agents as key developments behind this renewed interest. Its central question is how a pretrained or fine-tuned decision model can infer latent task rules and improve future behavior from interaction context, without updating its parameters at test time, especially when the environment is non-stationary. [S1] [S1] Paper overview: ICRL and non-stationarity The survey introduced in source S1 focuses on ICRL in settings where the environment changes over time rather than staying fixed. In the source’s framing, non-stationarity matters because an agent cannot assume t...