Influence-Based Reinforcement Learning for Intrinsically-Motivated Agents

Understand

Discovering successful coordinated behaviors is a central challenge in Multi-Agent Reinforcement Learning (MARL) since it requires exploring a joint action space that grows exponentially with the number of agents.

• In this paper, we propose a mechanism for achieving sufficient exploration and coordination in a team of agents.
• Specifically, agents are rewarded for contributing to a more diversified team behavior by employing proper intrinsic motivation functions.
• To learn meaningful coordination protocols, we structure agents' interactions by introducing a novel framework, where at each timestep, an agent simulates counterfactual rollouts of its policy and, through a sequence of computations, assesses the gap between other agents' current behaviors and their targets.