Phantom - A RL-driven Multi-Agent Framework to Model Complex Systems

Agent based modeling (ABM) is a computational approach to modeling complex systems by specifying the behavior of autonomous decision-making components or agents in the system and allowing the system dynamics to emerge from their interactions. Recent advances in the field of Multi-agent reinforcement learning (MARL) have made it feasible to study the equilibrium of complex environments where multiple agents learn simultaneously. However, most ABM frameworks are not RL-native, in that they do not offer concepts and interfaces that are compatible with the use of MARL to learn agent behaviors. In this paper, we introduce a new open-source framework, Phantom, to bridge the gap between ABM and MARL. Phantom is an RL-driven framework for agent-based modeling of complex multi-agent systems including, but not limited to, economic systems, markets and auctions. The framework aims to provide the tools to simplify the ABM specification in a MARL-compatible way-including features to encode dynamic partial observability, agent utility functions, heterogeneity in agent preferences or types, and constraints on the order in which agents can act (e.g. Stackelberg games, or more complex turn-taking environments). In this paper, we present the main features of Phantom and their design rationale.