We derive a new intrinsic social motivation for multi-agent reinforcement learning (MARL), in
which agents are rewarded for having causal influence over another agent's actions, where …

We propose a unified mechanism for achieving coordination and communication in Multi-
Agent Reinforcement Learning (MARL), through rewarding agents for having causal …

The majority of Multi-Agent Reinforcement Learning (MARL) literature equates the
cooperation of self-interested agents in mixed environments to the problem of social welfare …

In this paper, we study the problem of networked multi-agent reinforcement learning (MARL),
where a number of agents are deployed as a partially connected network and each interacts …

Sparse reward remains a valuable and challenging problem in multi-agent reinforcement
learning (MARL). This paper addresses this issue from a new perspective, ie, lazy agents …

A great challenge in cooperative decentralized multi-agent reinforcement learning (MARL) is
generating diversified behaviors for each individual agent when receiving only a team …

Effective communication can improve coordination in cooperative multi-agent reinforcement
learning (MARL). One popular communication scheme is exchanging agents' local …

Multi-agent reinforcement learning (MARL) has shown recent success in increasingly
complex fixed-team zero-sum environments. However, the real world is not zero-sum nor …

This paper investigates the use of intrinsic reward to guide exploration in multi-agent
reinforcement learning. We discuss the challenges in applying intrinsic reward to multiple …

Recently, a novel framework has been proposed for intrinsically motivated reinforcement
learning (IMRL) in which a learning agent is driven by rewards that include not only …