In this paper we incorporate autonomous agents' capability to perform parallel interactions into the cooperative search model, resulting in a new method which outperforms the...
We present a reinforcement learning game player that can interact with a General Game Playing system and transfer knowledge learned in one game to expedite learning in many other ...
of other components. This abstract presents an implemented illustration of such explicit component synergy and its usefulness in dynamic multi-agent environments. In such environme...
Multi-Agent Agreement problems (MAP) - the ability of a population of agents to search out and converge on a common state - are central issues in many multi-agent settings, from d...
straction is a useful tool for agents interacting with environments. Good state abstractions are compact, reuseable, and easy to learn from sample data. This paper and extends two...