Fulda, Nancy's Publications (detailed list)

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This page contains the titles and abstracts of papers written by author Fulda, Nancy, a member of the BYU Neural Networks and Machine Learning (NNML) Research Group. Postscript files are available for most papers. A more concise list is available.

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Predicting and Preventing Coordination Problems in Cooperative Learning Systems

  • Authors: Nancy Fulda and Dan Ventura
  • Abstract: We present a conceptual framework for creating Q-learning-based algorithms that converge to optimal equilibria in cooperative multiagent settings. This framework includes a set of conditions that are sufficient to guarantee optimal system performance. We demonstrate the efficacy of the framework by using it to analyze several well-known multi-agent learning algorithms and conclude by employing it as a design tool to construct a simple, novel multiagent learning algorithm.
  • Reference: In Proceedings of the International Joint Conference on Artificial Intelligence, pages 780–785, Hyderabad, India, January 2007.
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Learning a Rendezvous Task with Dynamic Joint Action Perception

  • Authors: Nancy Fulda and Dan Ventura
  • Abstract: Groups of reinforcement learning agents interacting in a common environment often fail to learn optimal behaviors. Poor performance is particularly common in environments where agents must coordinate with each other to receive rewards and where failed coordination attempts are penalized. This paper studies the effectiveness of the Dynamic Joint Action Perception (DJAP) algorithm on a grid-world rendezvous task with this characteristic. The effects of learning rate, exploration strategy, and training time on algorithm effectiveness are discussed. An analysis of the types of tasks for which DJAP learning is appropriate is also presented.
  • Reference: In Proceedings of the International Joint Conference on Neural Networks, pages 627–632, Vancouver, BC, July 2006.
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Incremental Policy Learning: An Equilibrium Selection Algorithm for Reinforcement Learning Agents with Common Interests

  • Authors: Nancy Fulda and Dan Ventura
  • Abstract: We present an equilibrium selection algorithm for reinforcement learning agents that incrementally adjusts the probabilityof executing each action based on the desirability of the outcome obtained in the last time step. The algorithm assumes that at least one coordination equilibrium exists and requires that the agents have a heuristic for determining whether or not the equilibrium was obtained. In deterministic environments with one or more strict coordination equilibria, the algorithm will learn to play an optimal equilibrium as long as the heuristic is accurate. Empirical data demonstrate that the algorithm is also effective in stochastic environments and is able to learn good joint policies when the heuristic’s parameters are estimated during learning, rather than known in advance.
  • Reference: In Proceedings of the International Joint Conference on Neural Networks, pages 1121–1126, July 2004.
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Target Sets: A Tool for Understanding and Predicting the Behavior of Interacting Q-learners

  • Authors: Nancy Fulda and Dan Ventura
  • Abstract: Reinforcement learning agents that interact in a common environment frequently affect each others’ perceived transition and reward distributions. This can result in convergence of the agents to a sub-optimal equilibrium or even to a solution that is not an equilibrium at all. Several modifications to the Q-learning algorithm have been proposed which enable agents to converge to optimal equilibria under specified conditions. This paper presents the concept of target sets as an aid to understanding why these modifications have been successful and as a tool to assist in the development of new modifications which are applicable in a wider range of situations.
  • Reference: In Proceedings of the Joint Conference on Information Sciences, pages 1549–1552, September 2003.
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Concurrently Learning Neural Nets: Encouraging Optimal Behavior in Reinforcement Learning Systems.

  • Authors: Nancy Fulda and Dan Ventura
  • Reference: In IEEE International Workshop on Soft Computing Techniques in Instrumentation, Measurement, and Related Applications (SCIMA), May 2003.
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Dynamic Joint Action Perception for Q-Learning Agents.

  • Authors: Nancy Fulda and Dan Ventura
  • Reference: In To Appear in Proceedings of the 2003 International Conference on Machine Learning and Applications, Los Angeles, CA, 2003.
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Towards Automatic Shaping in Robot Navigation.

  • Authors: Todd S. Peterson and Nancy Owens and James L. Carroll
  • Reference: In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2001.
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Memory-guided Exploration in Reinforcement Learning.

  • Authors: James L. Carroll and Todd S. Peterson and Nancy Owens
  • Reference: In Proceedings of the INNS-IEEE International Joint Conference on Neural Networks (IJCNN), 2001.
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Using a Reinforcement Learning Controller to Overcome Simulator/Environment Discrepancies.

  • Authors: Nancy Owens and Todd S. Peterson
  • Reference: In Proceedings of the IEEE Conference on Systems, Man, and Cybernetics, 2001.
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