Decision Transformer under Random Frame Dropping

Understand

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.