Log In Sign Up

Negotiating Team Formation Using Deep Reinforcement Learning

by   Yoram Bachrach, et al.

When autonomous agents interact in the same environment, they must often cooperate to achieve their goals. One way for agents to cooperate effectively is to form a team, make a binding agreement on a joint plan, and execute it. However, when agents are self-interested, the gains from team formation must be allocated appropriately to incentivize agreement. Various approaches for multi-agent negotiation have been proposed, but typically only work for particular negotiation protocols. More general methods usually require human input or domain-specific data, and so do not scale. To address this, we propose a framework for training agents to negotiate and form teams using deep reinforcement learning. Importantly, our method makes no assumptions about the specific negotiation protocol, and is instead completely experience driven. We evaluate our approach on both non-spatial and spatially extended team-formation negotiation environments, demonstrating that our agents beat hand-crafted bots and reach negotiation outcomes consistent with fair solutions predicted by cooperative game theory. Additionally, we investigate how the physical location of agents influences negotiation outcomes.


Collaboration of AI Agents via Cooperative Multi-Agent Deep Reinforcement Learning

There are many AI tasks involving multiple interacting agents where agen...

Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members

In many multi-agent settings, participants can form teams to achieve col...

Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferences

Multi-agent reinforcement learning (MARL) has shown recent success in in...

An attention model for the formation of collectives in real-world domains

We consider the problem of forming collectives of agents for real-world ...

Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition

The Pommerman Team Environment is a recently proposed benchmark which in...

Information Signal Design for Incentivizing Team Formation

We study the use of Bayesian persuasion (i.e., strategic use of informat...

Reinforcement Learning for Location-Aware Scheduling

Recent techniques in dynamical scheduling and resource management have f...