Cooperation

Interpreting whether multi-agent reinforcement learning (MARL) agents have successfully learned to coordinate with each other, versus finding some other way to exploit the reward function, is a longstanding problem. We develop a novel interpretability method for MARL based on concept bottlenecks, which enables detecting which agents are truly coordinating, which environments require coordination, and identifying lazy agents.

Joint attention is a critical component of human social cognition. In this paper, we ask whether a mechanism based on shared visual attention can be useful for improving multi-agent coordination and social learning.

My PhD Thesis spans both Social Reinforcement Learning and Affective Computing, investigating how affective and social intelligence can enhance machine learning algorithms, and how machine learning can enhance our ability to predict and understand human affective and social phenomena.

Social influence is a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning, through rewarding agents for having causal influence over other agents’ actions, thus increasing mutual information between agents’ actions. Optimizing for influence leads to agents learning emergent communication protocols. Unlike prior work, influence can be computed in a fully decentralized manner.