Team-Partitioned, Opaque-Transition Reinforcement Learning

@inproceedings{Stone1999TeamPartitionedOR,
  title={Team-Partitioned, Opaque-Transition Reinforcement Learning},
  author={Peter Stone and Manuela M. Veloso},
  booktitle={Agents},
  year={1999}
}
In this paper, we present a novel multi-agent learning paradigm called team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL introduces the concept of using action-dependent features to generalize the state space. In our work, we use a learned action-dependent feature space. 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… CONTINUE READING
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