Discovering Causality for Efficient Cooperation in Multi-Agent Environments

TL;DR

This paper explores how causality estimation can identify and penalize lazy agents in cooperative multi-agent reinforcement learning, leading to improved team performance and individual agent capabilities.

Contribution

It introduces the application of causality and Amortized Causal Discovery to enhance credit assignment and detect lazy agents in MARL environments.

Findings

Causality estimations improve credit assignment in MARL.

Detecting and penalizing lazy agents enhances team performance.

Amortized Causal Discovery efficiently identifies causal relations in MARL.

Abstract

In cooperative Multi-Agent Reinforcement Learning (MARL) agents are required to learn behaviours as a team to achieve a common goal. However, while learning a task, some agents may end up learning sub-optimal policies, not contributing to the objective of the team. Such agents are called lazy agents due to their non-cooperative behaviours that may arise from failing to understand whether they caused the rewards. As a consequence, we observe that the emergence of cooperative behaviours is not necessarily a byproduct of being able to solve a task as a team. In this paper, we investigate the applications of causality in MARL and how it can be applied in MARL to penalise these lazy agents. We observe that causality estimations can be used to improve the credit assignment to the agents and show how it can be leveraged to improve independent learning in MARL. Furthermore, we investigate how Amortized Causal Discovery can be used to automate causality detection within MARL environments. The results demonstrate that causality relations between individual observations and the team reward can be used to detect and punish lazy agents, making them develop more intelligent behaviours. This results in improvements not only in the overall performances of the team but also in their individual capabilities. In addition, results show that Amortized Causal Discovery can be used efficiently to find causal relations in MARL.