  Developing policies that can adjust to non-stationary environments is
essential for real-world reinforcement learning applications. However, learning
such adaptable policies in offline settings, with only a limited set of
pre-collected trajectories, presents significant challenges. A key difficulty
arises because the limited offline data makes it hard for the context encoder
to differentiate between changes in the environment dynamics and shifts in the
behavior policy, often leading to context misassociations. To address this
issue, we introduce a novel approach called Debiased Offline Representation for
fast online Adaptation (DORA). DORA incorporates an information bottleneck
principle that maximizes mutual information between the dynamics encoding and
the environmental data, while minimizing mutual information between the
dynamics encoding and the actions of the behavior policy. We present a
practical implementation of DORA, leveraging tractable bounds of the
information bottleneck principle. Our experimental evaluation across six
benchmark MuJoCo tasks with variable parameters demonstrates that DORA not only
achieves a more precise dynamics encoding but also significantly outperforms
existing baselines in terms of performance.
