cooperation

Aspiration-Based Reinforcement Learning in Repeated Interaction Games: An Overview

Too Many Cooks: Coordinating Multi-Agent Collaboration Through Inverse Planning

AAMAS 2020, May 9–13, Auckland, New Zealand Too Many Cooks: Coordinating Multi-agent Collaboration Through Inverse Planning∗ Extended Abstract Rose E. Wang∗ Sarah A. Wu∗ James A. Evans MIT MIT U. Chicago rewang@mit.edu sarahawu@mit.edu jevans@uchicago.edu Joshua B. Tenenbaum Davi...

Deception in Social Learning: A Multi-Agent Reinforcement Learning Perspective

Within the framework of Multi-Agent Reinforcement Learning, Social Learning is a new class of algorithms that enables agents to reshape the reward function of other agents with the goal of promoting cooperation and achieving higher global rewards in mixed-motive games. However, t...

Disentangling Interaction using Maximum Entropy Reinforcement Learning in Multi-Agent Systems

. Research on multi-agent interaction involving both mul- tiple artificial agents and humans is still in its infancy. Most recent ap- proaches have focused on environments with collaboration-focused human behavior, or providing only a small, defined set of situations. When deploy...