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- W1545262042 abstract "We present a novel multi-agent learning paradigm called team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL introduces the use of action-dependent features to generalize the state space. In our work, we use a learned action-dependent feature space to aid higher-level reinforcement learning. TPOT-RL is an effective technique to allow a team of agents to learn to cooperate towards the achievement of a specific goal. It is an adaptation of traditional RL methods that is applicable in complex, non-Markovian, multi-agent domains with large state spaces and limited training opportunities. TPOT-RL is fully implemented and has been tested in the robotic soccer domain, a complex, multi-agent framework. This paper presents the algorithmic details of TPOT-RL as well as empirical results demonstrating the effectiveness of the developed multi-agent learning approach with learned features." @default.
- W1545262042 created "2016-06-24" @default.
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- W1545262042 date "1999-01-01" @default.
- W1545262042 modified "2023-09-25" @default.
- W1545262042 title "Team-Partitioned, Opaque-Transition Reinforcement Learning" @default.
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- W1545262042 doi "https://doi.org/10.1007/3-540-48422-1_21" @default.
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