Model-Free Learning and Optimal Policy Design in Multiagent MDPs Under Probabilistic Agent Dropout

This work studies a multiagent Markov decision process (MDP) that can undergo agent dropout and the computation of policies for the postdropout system based on control and sampling of the predropout system. The central planner's objective is to find an optimal policy that maximizes the value of the expected system given a priori knowledge of the agents' dropout probabilities. For MDPs with a certain transition independence and reward separability structure, we assume that removing agents from the system forms a new MDP comprised of the remaining agents with new state and action spaces, transition dynamics that marginalize the removed agents, and rewards that are independent of the removed agents. We first show that under these assumptions, the value of the expected postdropout system can be represented by a single MDP; this “robust MDP” eliminates the need to evaluate all$2^{N}$realizations of the system, where$N$denotes the number of agents. More significantly, in a model-free context, it is shown that the robust MDP value can be estimated with samples generated by the predropout system, meaning that robust policies can be found before dropout occurs. This fact is used to propose a policy importance sampling (IS) routine that performs policy evaluation for dropout scenarios while controlling the existing system with good predropout policies. The policy IS routine produces value estimates for both the robust MDP and specific postdropout system realizations and is justified with exponential confidence bounds. Finally, the utility of this approach is verified in simulation, showing how structural properties of agent dropout can help a controller find good postdropout policies before dropout occurs.