Robustifying a Policy in Multi-Agent RL with Diverse Cooperative Behaviors and Adversarial Style Sampling for Assistive Tasks

Understand

Autonomous assistance of people with motor impairments is one of the most promising applications of autonomous robotic systems.

• Recent studies have reported encouraging results using deep reinforcement learning (RL) in the healthcare domain.
• Previous studies showed that assistive tasks can be formulated as multi-agent RL, wherein there are two agents: a caregiver and a care-receiver.
• However, policies trained in multi-agent RL are often sensitive to the policies of other agents.