Imitation learning for task allocation

At the heart of multi-robot task allocation lies the ability to compare multiple options in order to select the best. In some domains this utility evaluation is not straightforward, for example due to complex and unmodeled underlying dynamics or an adversary in the environment. Explicitly modeling these extrinsic influences well enough so that they can be accounted for in utility computation (and thus task allocation) may be intractable, but a human expert may be able to quickly gain some intuition about the form of the desired solution. We propose to harness the expert's intuition by applying imitation learning to the multi-robot task allocation domain. Using a market-based method, we steer the allocation process by biasing prices in the market according to a policy which we learn using a set of demonstrated allocations (the expert's solutions to a number of domain instances). We present results in two distinct domains: a disaster response scenario where a team of agents must put out fires that are spreading between buildings, and an adversarial game in which teams must make complex strategic decisions to score more points than their opponents.