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Faster Algorithms for Optimal Ex-Ante Coordinated Collusive Strategies in Extensive-Form Zero-Sum Games
We focus on the problem of finding an optimal strategy for a team of two...
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R-MADDPG for Partially Observable Environments and Limited Communication
There are several real-world tasks that would ben-efit from applying mul...
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The Communicative Multiagent Team Decision Problem: Analyzing Teamwork Theories and Models
Despite the significant progress in multiagent teamwork, existing resear...
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Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games
Many artificial intelligence (AI) applications often require multiple in...
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Team-maxmin equilibrium: efficiency bounds and algorithms
The Team-maxmin equilibrium prescribes the optimal strategies for a team...
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Analysing the behaviour of robot teams through relational sequential pattern mining
This report outlines the use of a relational representation in a Multi-A...
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RoboCup 2019 AdultSize Winner NimbRo: Deep Learning Perception, In-Walk Kick, Push Recovery, and Team Play Capabilities
Individual and team capabilities are challenged every year by rule chang...
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Coordination in Adversarial Sequential Team Games via Multi-Agent Deep Reinforcement Learning
Many real-world applications involve teams of agents that have to coordinate their actions to reach a common goal against potential adversaries. This paper focuses on zero-sum games where a team of players faces an opponent, as is the case, for example, in Bridge, collusion in poker, and collusion in bidding. The possibility for the team members to communicate before gameplay—that is, coordinate their strategies ex ante—makes the use of behavioral strategies unsatisfactory. We introduce Soft Team Actor-Critic (STAC) as a solution to the team's coordination problem that does not require any prior domain knowledge. STAC allows team members to effectively exploit ex ante communication via exogenous signals that are shared among the team. STAC reaches near-optimal coordinated strategies both in perfectly observable and partially observable games, where previous deep RL algorithms fail to reach optimal coordinated behaviors.
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