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2018 IEEE International Conference on Agents (ICA)
In complex environments, agents must be able to cooperate with previously unknown team-mates, and hence dynamically learn about other agents in the environment while searching for optimal actions. Previous works employ Monte Carlo Tree Search approaches. However, the search tree increases exponentially with the number of agents, and only scenarios with very small team sizes have been explored. Hence, in this paper we propose a history-based version of UCT Monte Carlo Tree Search, using a moredoi:10.1109/agents.2018.8460136 fatcat:i6dtyyprn5cmbisdqlhrk35cx4