  Controlling agents remotely with deep reinforcement learning~(DRL) in the
real world is yet to come. One crucial stepping stone is to devise RL
algorithms that are robust in the face of dropped information from corrupted
communication or malfunctioning sensors. Typical RL methods usually require
considerable online interaction data that are costly and unsafe to collect in
the real world. Furthermore, when applying to the frame dropping scenarios,
they perform unsatisfactorily even with moderate drop rates. To address these
issues, we propose Decision Transformer under Random Frame Dropping~(DeFog), an
offline RL algorithm that enables agents to act robustly in frame dropping
scenarios without online interaction. DeFog first randomly masks out data in
the offline datasets and explicitly adds the time span of frame dropping as
inputs. After that, a finetuning stage on the same offline dataset with a
higher mask rate would further boost the performance. Empirical results show
that DeFog outperforms strong baselines under severe frame drop rates like
90\%, while maintaining similar returns under non-frame-dropping conditions in
the regular MuJoCo control benchmarks and the Atari environments. Our approach
offers a robust and deployable solution for controlling agents in real-world
environments with limited or unreliable data.
