Agents
Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities
In fact, the interaction of these two aspects requires addressing the fact that each agent's own safety constraint requires information from all others.
Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games with Bandit Feedback
Our paper takes the first step to remove such assumptions.
Learning to Influence Human Behavior with Offline Reinforcement Learning
When interacting with people, AI agents do not just influence the state of the world - they also influence the actions people take in response to the agent, and even their underlying intentions and strategies.
Strategic Behavior in Two-sided Matching Markets with Recommendation-enhanced Preference-formation
Two-sided matching markets have long existed to pair agents in the absence of regulated exchanges.
Mixed-Initiative Multiagent Apprenticeship Learning for Human Training of Robot Teams - Supplementary Material
FireCommander is a strategic game, just like any other strategic games that you might have experienced before, such as: the soccer game series (e.g., FIFA or Pro Evolution Soccer (PES)), The World of Warcraft, The Age of Empires, or any other multi-character game that you can name.
Mixed-Initiative Multiagent Apprenticeship Learning for Human Training of Robot Teams
The key ingredient to MixTURE's success is automatically learning
RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization
Multi-agent systems are characterized by environmental uncertainty, varying policies of agents, and partial observability, which result in significant risks.
OFCOURSE: A Multi-Agent Reinforcement Learning Environment for Order Fulfillment
In particular, we model the integrated problem as a Markov game, wherein a team of agents learns a joint policy via interacting with a simulated environment.