  Human behaviors are regularized by a variety of norms or regulations, either
to maintain orders or to enhance social welfare. If artificially intelligent
(AI) agents make decisions on behalf of human beings, we would hope they can
also follow established regulations while interacting with humans or other AI
agents. However, it is possible that an AI agent can opt to disobey the
regulations (being defective) for self-interests. In this paper, we aim to
answer the following question: Consider a multi-agent decentralized
environment. Agents make decisions in complete isolation of other agents. Each
agent knows the state of its own MDP and its own actions but it does not know
the states and the actions taken by other players. There is a set of
regulations for all agents to follow. Although most agents are benign and will
comply to regulations but not all agents are compliant at first, can we develop
a framework such that it is in the self-interest of non-compliant agents to
comply after all?. We first introduce the problem as Regulation Enforcement and
formulate it using reinforcement learning and game theory under the scenario
where agents make decisions in complete isolation of other agents. We then
propose a solution based on the key idea that although we could not alter how
defective agents choose to behave, we can, however, leverage the aggregated
power of compliant agents to boycott the defective ones. We conducted simulated
experiments on two scenarios: Replenishing Resource Management Dilemma and
Diminishing Reward Shaping Enforcement, using deep multi-agent reinforcement
learning algorithms. We further use empirical game-theoretic analysis to show
that the method alters the resulting empirical payoff matrices in a way that
promotes compliance (making mutual compliant a Nash Equilibrium).
