# Peter Stone's Selected Publications

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## Team-Partitioned, Opaque-Transition Reinforcement Learning

Peter Stone and Manuela Veloso. Team-Partitioned, Opaque-Transition Reinforcement Learning. In Minoru Asada and Hiroaki Kitano, editors, RoboCup-98: Robot Soccer World Cup II, Lecture Notes in Artificial Intelligence, pp. 261–72, Springer Verlag, Berlin, 1999. Also in Proceedings of the Third International Conference on Autonomous Agents, 1999

### 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.

### BibTeX Entry

@InCollection(LNAI98-tpot-rl,
Author="Peter Stone and Manuela Veloso",
Title="Team-Partitioned, Opaque-Transition Reinforcement Learning",
booktitle= "{R}obo{C}up-98: Robot Soccer World Cup {II}",
series="Lecture Notes in Artificial Intelligence",
volume="1604",
pages="261--72",
note= "Also in {\it Proceedings of the Third International Conference on
Autonomous Agents}, 1999",
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
{\it 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
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.
},
)


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