task-decomposition

Too Many Cooks: Coordinating Multi-Agent Collaboration Through Inverse Planning

AAMAS 2020, May 9–13, Auckland, New Zealand Too Many Cooks: Coordinating Multi-agent Collaboration Through Inverse Planning∗ Extended Abstract Rose E. Wang∗ Sarah A. Wu∗ James A. Evans MIT MIT U. Chicago rewang@mit.edu sarahawu@mit.edu jevans@uchicago.edu Joshua B. Tenenbaum Davi...

Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning

Reinforcement learning (RL) methods usually treat reward functions as black boxes. As such, these methods must extensively interact with the environment in order to discover rewards and optimal policies. In most RL applications, however, users have to program the reward function...

Verifiable and Compositional Reinforcement Learning Systems

However, there remain significant barriers to their deploy- ment in safety-critical engineering applications. Autonomous We propose a framework for verifiable and compositional vehicles, manufacturing robotics, and power systems manage- reinforcement learning (RL) in which a coll...