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1 |
Negotiating team formation using deep reinforcement learning
مذاکره در مورد تشکیل تیم با استفاده از یادگیری تقویت عمیق-2020 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. Keywords: Multi-agent systems | Team formation | Coalition formation | Reinforcement learning | Deep learning | Cooperative games | Shapley value |
مقاله انگلیسی |
2 |
The dynamics of reinforcement social learning in networked cooperative multiagent systems
پویایی یادگیری اجتماعی تقویت در سیستم های چندگانه تعاونی شبکه ای-2017 Multiagent coordination in cooperative multiagent systems, as one of the fundamental problems in multiagent
systems, and has been widely studied in the literature. In real environments, the interactions among agents are
usually sparse and regulated by their underlying network structure, which, however, has received relatively few
attentions in previous work. To this end, we firstly systematically investigate the multiagent coordination
problems in cooperative environments under the networked social learning framework under four representa
tive topologies. A networked social learning framework consists of a population of agents where each agent
interacts with another agent randomly selected from its neighborhood in each round. Each agent updates its
learning policy through repeated interactions with its neighbors via both individual learning and social learning.
It is not clear a priori whether all agents are able to learn towards a consistent optimal coordination policy. Two
types of learners are proposed: individual action learner and joint action learner. We evaluate the learning
performances of both learners extensively in different cooperative (both single-stage and Markov) games.
Besides, the influence of different factors (network topologies, different types of games, different topology
parameters) is investigated and analyzed and new insights are obtained.
Keywords: Multiagent social learning | Multiagent coordination | Cooperative games |
مقاله انگلیسی |