2023
Multi-Agent Reinforcement Learning with Epistemic Priors
Walker T, Ide J, Choi M, Guarino M, Alcedo K. Multi-Agent Reinforcement Learning with Epistemic Priors. 2023, 00: 2514-2518. DOI: 10.1109/codit58514.2023.10284342.Peer-Reviewed Original Research
2022
Hierarchical Reinforcement Learning for Air Combat at DARPA's AlphaDogfight Trials
Pope A, Ide J, Miovi D, Diaz H, Twedt J, Alcedo K, Walker T, Rosenbluth D, Ritholtz L, Javorsek D. Hierarchical Reinforcement Learning for Air Combat at DARPA's AlphaDogfight Trials. IEEE Transactions On Artificial Intelligence 2022, 4: 1371-1385. DOI: 10.1109/tai.2022.3222143.Peer-Reviewed Original ResearchAir combatLow-level policiesHierarchical reinforcement learningField of roboticsAdoption of AIHierarchical Deep ReinforcementContinuous control problemsDeep reinforcementArtificial intelligenceReward shapingContinuous state spaceReinforcement learningState spaceAutonomous controlExpert knowledgeCombat systemExpert pilotsAIImportant challengeControl problemRoboticsIntelligenceLearningComplexitySpace
2018
Oxytocin attenuates trust as a subset of more general reinforcement learning, with altered reward circuit functional connectivity in males
Ide J, Nedic S, Wong K, Strey S, Lawson E, Dickerson B, Wald L, La Camera G, Mujica-Parodi L. Oxytocin attenuates trust as a subset of more general reinforcement learning, with altered reward circuit functional connectivity in males. NeuroImage 2018, 174: 35-43. PMID: 29486321, DOI: 10.1016/j.neuroimage.2018.02.035.Peer-Reviewed Original ResearchConceptsUltra-high field fMRIPre-existing beliefsReinforcement learningBrain encodingGeneral reinforcement learningNeuroeconomic taskIntranasal oxytocinFace of informationField fMRIFeedback learningFMRI activationExperimental paradigmNegative rewardsOrbitofrontal cortexSocial learningReward circuitBehavioral trajectoriesFunctional connectivityConnectivity analysisSocial contextSubjects' behaviorSocial relationshipsBehavioral effectsLearningBayesian expectation
2017
Classification of Cocaine Dependents from fMRI Data Using Cluster-Based Stratification and Deep Learning
Santos J, Savii R, Ide J, Li C, Quiles M, Basgalupp M. Classification of Cocaine Dependents from fMRI Data Using Cluster-Based Stratification and Deep Learning. Lecture Notes In Computer Science 2017, 10404: 298-313. DOI: 10.1007/978-3-319-62392-4_22.Peer-Reviewed Original ResearchDeep learningDeep learning methodsDeep neural networksDeep belief networkSmall data setsComputational visionClassification of pathologiesBelief networkFMRI classificationVoice recognitionNeural networkLearning methodsRobust trainingBrain decodingSmall dataData setsLearningCocaine dependenceNovel stratification methodTraditional techniquesNetworkClassificationCocaine dependentsNon-addicted individualsDrug use