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Team-Partitioned, Opaque-Transition Reinforcement Learning (1999)
Peter Stone
and Manuela Veloso
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 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.
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Citation:
In Minoru Asada and Hiroaki Kitano, editors,
RoboCup-98: Robot Soccer World Cup II
, Lecture Notes in Artificial Intelligence, 1604, 261-72, Berlin, 1999. Springer Verlag. Also in
Proceedings of the Third International Conference on Autonomous Agents
, 1999.
Bibtex:
@InCollection{LNAI98-tpot-rl, title={Team-Partitioned, Opaque-Transition Reinforcement Learning}, author={Peter Stone and Manuela Veloso}, booktitle={RoboCup-98: Robot Soccer World Cup II}, volume={1604}, editor={Minoru Asada and Hiroaki Kitano}, series={Lecture Notes in Artificial Intelligence}, address={Berlin}, publisher={Springer Verlag}, pages={261-72}, note={Also in
Proceedings of the Third International Conference on Autonomous Agents
, 1999}, url="http://nn.cs.utexas.edu/?LNAI98-tpot-rl", year={1999} }
People
Peter Stone
pstone [at] cs utexas edu
Areas of Interest
Simulated Robot Soccer
Reinforcement Learning
Other Areas