Title: Deep Reinforcement Learning for Event-Driven Multi-Agent Decision Processes,
Abstract: The incorporation of macro-actions (temporally extended actions) into
multi-agent decision problems has the potential to address the curse of
dimensionality associated with such decision problems. Since macro-actions last
for stochastic durations, multiple agents executing decentralized policies in
cooperative environments must act asynchronously. We present an algorithm that
modifies Generalized Advantage Estimation for temporally extended actions,
allowing a state-of-the-art policy optimization algorithm to optimize policies
in Dec-POMDPs in which agents act asynchronously. We show that our algorithm is
capable of learning optimal policies in two cooperative domains, one involving
real-time bus holding control and one involving wildfire fighting with unmanned
aircraft. Our algorithm works by framing problems as "event-driven decision
processes," which are scenarios where the sequence and timing of actions and
events are random and governed by an underlying stochastic process. In addition
to optimizing policies with continuous state and action spaces, our algorithm
also facilitates the use of event-driven simulators, which do not require time
to be discretized into time-steps. We demonstrate the benefit of using
event-driven simulation in the context of multiple agents taking asynchronous
actions. We show that fixed time-step simulation risks obfuscating the sequence
in which closely-separated events occur, adversely affecting the policies
learned. Additionally, we show that arbitrarily shrinking the time-step scales
poorly with the number of agents.