  Decentralized multi-agent reinforcement learning (MARL) algorithms have
become popular in the literature since it allows heterogeneous agents to have
their own reward functions as opposed to canonical multi-agent Markov Decision
Process (MDP) settings which assume common reward functions over all agents. In
this work, we follow the existing work on collaborative MARL where agents in a
connected time varying network can exchange information among each other in
order to reach a consensus. We introduce vulnerabilities in the consensus
updates of existing MARL algorithms where agents can deviate from their usual
consensus update, who we term as adversarial agents. We then proceed to provide
an algorithm that allows non-adversarial agents to reach a consensus in the
presence of adversaries under a constrained setting.
