  Offline Reinforcement Learning (RL) has emerged as a promising framework for
learning policies without active interactions, making it especially appealing
for autonomous driving tasks. Recent successes of Transformers inspire casting
offline RL as sequence modeling, which performs well in long-horizon tasks.
However, they are overly optimistic in stochastic environments with incorrect
assumptions that the same goal can be consistently achieved by identical
actions. In this paper, we introduce an UNcertainty-awaRE deciSion Transformer
(UNREST) for planning in stochastic driving environments without introducing
additional transition or complex generative models. Specifically, UNREST
estimates state uncertainties by the conditional mutual information between
transitions and returns, and segments sequences accordingly. Discovering the
`uncertainty accumulation' and `temporal locality' properties of driving
environments, UNREST replaces the global returns in decision transformers with
less uncertain truncated returns, to learn from true outcomes of agent actions
rather than environment transitions. We also dynamically evaluate environmental
uncertainty during inference for cautious planning. Extensive experimental
results demonstrate UNREST's superior performance in various driving scenarios
and the power of our uncertainty estimation strategy.
