Combining planning with reinforcement learning for multi-robot task allocation

We describe an approach to the multi-robot task allocation (MRTA) problem in which a group of robots must perform tasks that arise continuously, at arbitrary locations across a large space. A dynamic scheduling algorithm is derived in which proposed plans are evaluated using a combination of short-term lookahead and a value function acquired by reinforcement learning. We demonstrate that this dynamic scheduler can learn not only to allocate robots to tasks efficiently, but also to position the robots appropriately in readiness for new tasks (tactical awareness), and conserve resources over the long run (strategic awareness).