2016 · 53 citations · 15 references
EngineeringAutomotive EngineeringAutonomous SystemsAutonomous VehiclesGame TheoryMarkov Decision ProcessesIntelligent Transportation SystemsPartially Observable Markov Decision ProcessesPolicy LearningReinforcement LearningReinforcement Learning FoundationsTraffic EngineeringDriving Behavior ModelingHierarchical Reinforcement LearningMarkov PropertyReinforcementReinforcement Learning ModelsReinforcement Learning TheoryTraffic Interaction ModelsTraffic Simulation Modeling
This paper describes a game theoretical model of traffic where multiple drivers interact with each other. The model is developed using hierarchical reasoning, a game theoretical model of human behavior, and reinforcement learning. It is assumed that the drivers can observe only a partial state of the traffic they are in and therefore although the environment satisfies the Markov property, it appears as non-Markovian to the drivers. Hence, each driver implicitly has to find a policy, i.e. a mapping from observations to actions, for a Partially Observable Markov Decision Process. In this paper, a computationally tractable solution to this problem is provided by employing hierarchical reasoning together with a suitable reinforcement learning algorithm. Simulation results are reported, which demonstrate that the resulting driver models provide reasonable behavior for the given traffic scenarios.
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Reinforcement Learning: An Introduction
Richard S. Sutton, Andy Barto · IEEE Transactions on Neural Networks · 1998 · 27.1K citations
Reinforcement Learning: An Introduction
IEEE Transactions on Neural Networks · 2005 · 25.8K citations