Deep Multi-agent Reinforcement Learning in a Common-Pool Resource System

Abstract

In complex social-ecological systems, multiple agents with diverse objectives take actions that affect the long-term dynamics of the system. Common pool resources are a subset of such systems, where property rights are typically poorly defined and dynamics are unknown a priori, creating a social dilemma reflected by the well-known 'tragedy of the commons.' In this paper, we investigated the efficacy of deep reinforcement learning in a multi-agent setting of a common pool resource system. We used an abstract mathematical model of the system, represented as a partially-observable general-sum Markov game. In the first set of experiments, the independent agents used a deep Q-Network with discret..