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Physical Sciences and Mathematics Commons™
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Articles 1 - 3 of 3
Full-Text Articles in Physical Sciences and Mathematics
Dynamic Coalition Formation Under Uncertainty, Daylon J. Hooper, Gilbert L. Peterson, Brett J. Borghetti
Dynamic Coalition Formation Under Uncertainty, Daylon J. Hooper, Gilbert L. Peterson, Brett J. Borghetti
Faculty Publications
Coalition formation algorithms are generally not applicable to real-world robotic collectives since they lack mechanisms to handle uncertainty. Those mechanisms that do address uncertainty either deflect it by soliciting information from others or apply reinforcement learning to select an agent type from within a set. This paper presents a coalition formation mechanism that directly addresses uncertainty while allowing the agent types to fall outside of a known set. The agent types are captured through a novel agent modeling technique that handles uncertainty through a belief-based evaluation mechanism. This technique allows for uncertainty in environmental data, agent type, coalition value, and …
Fuzzy State Aggregation And Policy Hill Climbing For Stochastic Environments, Dean C. Wardell, Gilbert L. Peterson
Fuzzy State Aggregation And Policy Hill Climbing For Stochastic Environments, Dean C. Wardell, Gilbert L. Peterson
Faculty Publications
Reinforcement learning is one of the more attractive machine learning technologies, due to its unsupervised learning structure and ability to continually learn even as the operating environment changes. Additionally, by applying reinforcement learning to multiple cooperative software agents (a multi-agent system) not only allows each individual agent to learn from its own experience, but also opens up the opportunity for the individual agents to learn from the other agents in the system, thus accelerating the rate of learning. This research presents the novel use of fuzzy state aggregation, as the means of function approximation, combined with the fastest policy hill …
Fuzzy State Aggregation And Off-Policy Reinforcement Learning For Stochastic Environments, Dean C. Wardell, Gilbert L. Peterson
Fuzzy State Aggregation And Off-Policy Reinforcement Learning For Stochastic Environments, Dean C. Wardell, Gilbert L. Peterson
Faculty Publications
Reinforcement learning is one of the more attractive machine learning technologies, due to its unsupervised learning structure and ability to continually learn even as the environment it is operating in changes. This ability to learn in an unsupervised manner in a changing environment is applicable in complex domains through the use of function approximation of the domain’s policy. The function approximation presented here is that of fuzzy state aggregation. This article presents the use of fuzzy state aggregation with the current policy hill climbing methods of Win or Lose Fast (WoLF) and policy-dynamics based WoLF (PD-WoLF), exceeding the learning rate …