Related papers: Negotiating Team Formation Using Deep Reinforcement Learning

Negotiating Team Formation Using Deep Reinforcement Learning

URL: http://arxiv.org/abs/2010.10380v1
Date: Tue, 20 Oct 2020 15:41:23 GMT
Title: Negotiating Team Formation Using Deep Reinforcement Learning
Authors: Yoram Bachrach, Richard Everett, Edward Hughes, Angeliki Lazaridou, Joel Z. Leibo, Marc Lanctot, Michael Johanson, Wojciech M. Czarnecki, Thore Graepel
Abstract summary: We propose a framework for training agents to negotiate and form teams using deep reinforcement learning. We evaluate our approach on both non-spatial and spatially extended team-formation negotiation environments.
Score: 32.066083116314815
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

Related papers

Be Selfish, But Wisely: Investigating the Impact of Agent Personality in Mixed-Motive Human-Agent Interactions [24.266490660606497]
We find that self-play RL fails to learn the value of compromise in a negotiation. We modify the training procedure in two novel ways to design agents with diverse personalities and analyze their performance with human partners. We find that although both techniques show promise, a selfish agent, which maximizes its own performance while also avoiding walkaways, performs superior to other variants by implicitly learning to generate value for both itself and the negotiation partner.
arXiv Detail & Related papers (2023-10-22T20:31:35Z)
Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation [52.930183136111864]
We propose using scorable negotiation to evaluate Large Language Models (LLMs) To reach an agreement, agents must have strong arithmetic, inference, exploration, and planning capabilities. We provide procedures to create new games and increase games' difficulty to have an evolving benchmark.
arXiv Detail & Related papers (2023-09-29T13:33:06Z)
ProAgent: Building Proactive Cooperative Agents with Large Language Models [89.53040828210945]
ProAgent is a novel framework that harnesses large language models to create proactive agents. ProAgent can analyze the present state, and infer the intentions of teammates from observations. ProAgent exhibits a high degree of modularity and interpretability, making it easily integrated into various coordination scenarios.
arXiv Detail & Related papers (2023-08-22T10:36:56Z)
Adaptation and Communication in Human-Robot Teaming to Handle Discrepancies in Agents' Beliefs about Plans [13.637799815698559]
We provide an online execution algorithm based on Monte Carlo Tree Search for the agent to plan its action. We show that our agent is better equipped to work in teams without the guarantee of a shared mental model.
arXiv Detail & Related papers (2023-07-07T03:05:34Z)
Language of Bargaining [60.218128617765046]
We build a novel dataset for studying how the use of language shapes bilateral bargaining. Our work also reveals linguistic signals that are predictive of negotiation outcomes.
arXiv Detail & Related papers (2023-06-12T13:52:01Z)
Learning to Cooperate with Unseen Agent via Meta-Reinforcement Learning [4.060731229044571]
Ad hoc teamwork problem describes situations where an agent has to cooperate with previously unseen agents to achieve a common goal. One could implement cooperative skills into an agent by using domain knowledge to design the agent's behavior. We apply meta-reinforcement learning (meta-RL) formulation in the context of the ad hoc teamwork problem.
arXiv Detail & Related papers (2021-11-05T12:01:28Z)
Targeted Data Acquisition for Evolving Negotiation Agents [6.953246373478702]
Successful negotiators must learn how to balance optimizing for self-interest and cooperation. Current artificial negotiation agents often heavily depend on the quality of the static datasets they were trained on. We introduce a targeted data acquisition framework where we guide the exploration of a reinforcement learning agent.
arXiv Detail & Related papers (2021-06-14T19:45:59Z)
On the Critical Role of Conventions in Adaptive Human-AI Collaboration [73.21967490610142]
We propose a learning framework that teases apart rule-dependent representation from convention-dependent representation. We experimentally validate our approach on three collaborative tasks varying in complexity.
arXiv Detail & Related papers (2021-04-07T02:46:19Z)
On Emergent Communication in Competitive Multi-Agent Teams [116.95067289206919]
We investigate whether competition for performance from an external, similar agent team could act as a social influence. Our results show that an external competitive influence leads to improved accuracy and generalization, as well as faster emergence of communicative languages.
arXiv Detail & Related papers (2020-03-04T01:14:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.