Policy Synthesis for Collective Dynamics

In this paper we consider the problem of policy synthesis for systems of large numbers of simple interacting agents where dynamics of the system change through information spread via broadcast communication. By modifying the existing modelling language Carma and giving it a semantics in terms of continuous time Markov decision processes we introduce a natural way of formulating policy synthesis problems for such systems. However, solving policy synthesis problems is difficult since all non-trivial models result in very large state spaces. To combat this we propose an approach exploiting the results on fluid approximations of continuous time Markov chains to obtain estimates of optimal policies.