Self-Confirming Transformer for Locally Consistent Online Adaptation in
  Multi-Agent Reinforcement Learning
Tao Li Juan Guevara Xinghong Xie Quanyan Zhu
Offline reinforcement learning (RL) leverages previously collected data to
extract policies that return satisfying performance in online environments.
However, offline RL suffers from the distribution shift between the offline
dataset and the online environment. In the multi-agent RL (MARL) setting, this
distribution shift may arise from the nonstationary opponents (exogenous agents
beyond control) in the online testing who display distinct behaviors from those
recorded in the offline dataset. Hence, the key to the broader deployment of
offline MARL is the online adaptation to nonstationary opponents. Recent
advances in large language models have demonstrated the surprising
generalization ability of the transformer architecture in sequence modeling,
which prompts one to wonder \textit{whether the offline-trained transformer
policy adapts to nonstationary opponents during online testing}. This work
proposes the self-confirming loss (SCL) in offline transformer training to
address the online nonstationarity, which is motivated by the self-confirming
equilibrium (SCE) in game theory. The gist is that the transformer learns to
predict the opponents' future moves based on which it acts accordingly. As a
weaker variant of Nash equilibrium (NE), SCE (equivalently, SCL) only requires
local consistency: the agent's local observations do not deviate from its
conjectures, leading to a more adaptable policy than the one dictated by NE
focusing on global optimality. We evaluate the online adaptability of the
self-confirming transformer (SCT) by playing against nonstationary opponents
employing a variety of policies, from the random one to the benchmark MARL
policies. Experimental results demonstrate that SCT can adapt to nonstationary
opponents online, achieving higher returns than vanilla transformers and
offline MARL baselines.