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Bayesian Reinforcement Learning: A Survey
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Statistical Signal Processing
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Reinforcement Learning in Robotics: A Survey
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Regularized Fitted Q-Iteration: Application to Planning
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Smarter Sampling in Model-Based Bayesian Reinforcement Learning
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Bayesian Multitask Inverse Reinforcement Learning
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Bayesian Nonparametric Inverse Reinforcement Learning
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Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis
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Learning to Predict by the Methods of Temporal Differences
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Adaptive dual control methods: An overview
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Reinforcement learning with limited reinforcement: Using Bayes risk for active learning in POMDPs
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Natural actor–critic algorithms
inferredcites →https://doi.org/10.1016/j.ins.2011.01.001openalex-snapshot
Hessian matrix distribution for Bayesian policy gradient reinforcement learning
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An analysis of model-based Interval Estimation for Markov Decision Processes
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Bayesian Inverse Reinforcement Learning
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Kernel Methods for Pattern Analysis
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A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes
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Survey of adaptive dual control methods
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ON THE LIKELIHOOD THAT ONE UNKNOWN PROBABILITY EXCEEDS ANOTHER IN VIEW OF THE EVIDENCE OF TWO SAMPLES
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Optimistic planning for belief-augmented Markov Decision Processes
inferredcites →https://doi.org/10.1109/cdc.1992.371636openalex-snapshot
Bayesian adaptive control of time varying systems
inferredcites →https://doi.org/10.1109/icassp.2011.5946754openalex-snapshot
Bayesian reinforcement learning for POMDP-based dialogue systems
inferredcites →https://doi.org/10.1109/icca.2003.1229231openalex-snapshot
Adaptive control of nonlinear stochastic systems by particle filtering
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Improving the efficiency of Bayesian inverse reinforcement learning
inferredcites →https://doi.org/10.1109/iros.2009.5354013openalex-snapshot
Bayesian reinforcement learning in continuous POMDPs with gaussian processes
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Robust Adaptive Markov Decision Processes: Planning with Model Uncertainty
inferredcites →https://doi.org/10.1109/robot.1997.606886openalex-snapshot
A comparison of direct and model-based reinforcement learning
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inferredcites →https://doi.org/10.1109/robot.2008.4543641openalex-snapshot
Bayesian reinforcement learning in continuous POMDPs with application to robot navigation
inferredcites →https://doi.org/10.1111/j.2517-6161.1979.tb01068.xopenalex-snapshot
Bandit Processes and Dynamic Allocation Indices
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Apprenticeship learning via inverse reinforcement learning
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Reinforcement learning with Gaussian processes
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A theoretical analysis of Model-Based Interval Estimation
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Bayesian sparse sampling for on-line reward optimization
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An analytic solution to discrete Bayesian reinforcement learning
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Dyna, an integrated architecture for learning, planning, and reacting
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Bayesian actor-critic algorithms
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Online kernel selection for Bayesian reinforcement learning
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Automatic ad format selection via contextual bandits
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TD-Gammon, a Self-Teaching Backgammon Program, Achieves Master-Level Play
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Maximum Entropy Inverse Reinforcement Learning
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Robust Control of Markov Decision Processes with Uncertain Transition Matrices
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Benchmarking for Bayesian Reinforcement Learning
inferredcites →https://doi.org/10.1561/9781601986276openalex-snapshot
Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems
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Experiments with Infinite-Horizon, Policy-Gradient Estimation
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PAC-Bayesian Analysis of the Exploration-Exploitation Trade-off
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Analysis of Thompson Sampling for the multi-armed bandit problem
inferredcites →https://doi.org/10.48550/arxiv.1202.3717openalex-snapshot
PAC-Bayesian Policy Evaluation for Reinforcement Learning
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Variance-Based Rewards for Approximate Bayesian Reinforcement Learning
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A Bayesian Sampling Approach to Exploration in Reinforcement Learning
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Model-Based Bayesian Reinforcement Learning in Large Structured Domains
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Near-Optimal BRL using Optimistic Local Transitions
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Apprenticeship Learning using Inverse Reinforcement Learning and Gradient Methods
inferredcites →https://doi.org/10.48550/arxiv.1209.3353openalex-snapshot
Further Optimal Regret Bounds for Thompson Sampling
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Model-Based Bayesian Exploration
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A Greedy Approximation of Bayesian Reinforcement Learning with Probably Optimistic Transition Model
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(More) Efficient Reinforcement Learning via Posterior Sampling
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An Information-Theoretic Analysis of Thompson Sampling
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Thompson Sampling for Learning Parameterized Markov Decision Processes
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Bayesian policy gradient and actor-critic algorithms
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The Sample Complexity of Exploration in the Multi-Armed Bandit Problem
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A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes
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Model-based Bayesian reinforcement learning with generalized priors
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A unifying framework for computational reinforcement learning theory
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Bayesian Policy Gradient Algorithms
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Gaussian enhanced deep reinforcement learning for USV navigation with scarce environmental priors
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Discrete space reinforcement learning algorithm based on support vector machine classification
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Reinforcement learning versus evolutionary computation: A survey on hybrid algorithms
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A model for system uncertainty in reinforcement learning
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Adaptive traffic signal control with actor-critic methods in a real-world traffic network with different traffic disruption events
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Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities
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An intelligent multi-agent system for last-mile logistics
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Navigating uncertainty: Safety solutions for autonomous vehicle integration into mixed-mode mobility
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Predictive Bayesian Modelling in Cognitive Sciences
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Comparative Analysis of Reinforcement Learning Algorithms for Finding Reaction Pathways: Insights from a Large Benchmark Data Set
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Bayesian‐Belief Direct Policy Search for Adaptive Water Supply Planning With Endogenous Learning
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Data-driven Reynolds-averaged turbulence modeling with generalizable non-linear correction and uncertainty quantification using Bayesian deep learning
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A review on reinforcement learning algorithms and applications in supply chain management
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The Influence of Guided Error-Based Learning on Motor Skills Self-Efficacy and Achievement
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Discovering urban mobility structure: a spatio-temporal representational learning approach
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Transient analysis and real-time control of geometric serial lines with residence time constraints
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Information-directed policy sampling for episodic Bayesian Markov decision processes
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Physics-informed gradient boosting for particle size measurement in turbid media
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The dynamics of pain avoidance: the exploration–exploitation dilemma
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Risk-aware multi-armed bandit problem with application to portfolio selection
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Bayesian causal inference: a critical review
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Task learning reveals neural signatures of internal models in rodent prefrontal cortex
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Reinforcement Learning-Based Adaptive Transmission in Time-Varying Underwater Acoustic Channels
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Q-Learning Algorithms: A Comprehensive Classification and Applications
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Electric Water Heaters Management via Reinforcement Learning With Time-Delay in Isolated Microgrids
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RLOps: Development Life-Cycle of Reinforcement Learning Aided Open RAN
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The Elements of Flexibility for Task-Performing Systems
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Importance-Weighted Variational Inference Model Estimation for Offline Bayesian Model-Based Reinforcement Learning
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Boosting Policy Learning in Reinforcement Learning via Adaptive Intrinsic Reward Regulation
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ADF-SL: An Adaptive and Fair Scheme for Smart Learning Task Distribution
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Explainable Deep Reinforcement Learning for Space Situational Awareness: Counterfactual Explanation Approach
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On-line Planning on Active SLAM-Based Robot Olfaction for Gas Distribution Mapping
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Learning Adaptation and Generalization from Human-Inspired Meta-Reinforcement Learning Using Bayesian Knowledge and Analysis
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An Information-Theoretic Analysis of Bayesian Reinforcement Learning
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Dirichlet Process Mixture Model Based Nonparametric Bayesian Modeling and Variational Inference
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Traffic Flow Model Based Bayesian Reinforcement Learning For Traffic Signal Control
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Modelling uncertainty in reinforcement learning
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Optimal Control of Probabilistic Dynamics Models via Mean Hamiltonian Minimization
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AURELIUS: Agentic Uncertainty-Reasoning & Learning with Hierarchical Bayesian Multi-Agents Unified System for Handling Non-Determinism in HealthTech
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In-Network Machine Learning Using Programmable Network Devices: A Survey
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Semantic Communication with Bayesian Reinforcement Learning in Edge Computing
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Preparing for the Unexpected: Diversity Improves Planning Resilience in Evolutionary Algorithms
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Robust Deep Reinforcement Learning for Underwater Navigation with Unknown Disturbances
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Research on Optimization of Attack and Defence Area of Multi-UAV Based on Reinforcement Learning
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A Survey of Hierarchical Reinforcement Learning for Combinatorial Optimization in Logistics
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Reinforcement Learning Compensation based PD Control for Inverted Pendulum
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Neural Network and Reinforcement Learning Based Energy Management Strategy for Battery/Supercapacitor HEV
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Monte-Carlo Bayesian Reinforcement Learning Using a Compact Factored Representation
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Uncertainty Aware System Identification with Universal Policies
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Posterior Sampling for Anytime Motion Planning on Graphs with Expensive-to-Evaluate Edges
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Failure is an option: Task and Motion Planning with Failing Executions
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Adaptive Test Prioritization for Drone Communication Software: A Constrained Bayesian Contextual Bandit Approach*
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Dynamic Replanning with Posterior Sampling
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Safe Continuous Control with Constrained Model-Based Policy Optimization
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Bayesian Residual Policy Optimization: : Scalable Bayesian Reinforcement Learning with Clairvoyant Experts
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UF-RNN: Real-Time Adaptive Motion Generation Using Uncertainty-Driven Foresight Prediction
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Disentangling Uncertainty for Safe Social Navigation using Deep Reinforcement Learning
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MC2RAM: Markov Chain Monte Carlo Sampling in SRAM for Fast Bayesian Inference
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Distributed Dynamic Resource Management and Pricing in the IoT Systems With Blockchain-as-a-Service and UAV-Enabled Mobile Edge Computing
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Optimizing Age of Information and Security of the Next-Generation Internet of Everything Systems
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Stochastic-Stackelberg-Game-Based Edge Service Selection for Massive IoT Networks
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Digital-Twin-Based 3-D Map Management for Edge-Assisted Device Pose Tracking in Mobile AR
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RIS-Assisted Physical-Layer Key Generation for D2D Communications With Correlated and Imperfect Channels
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Transitions: A Protocol-Independent View of the Future Internet
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Cautious Reinforcement Learning via Distributional Risk in the Dual Domain
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State-of-the-Art Integrated Guidance and Control Systems in Unmanned Vehicles: A Review
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Advancing 6G: Survey for Explainable AI on Communications and Network Slicing
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Use of Improper Prior Distributions for Bayesian Reliability Analysis
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Validation and Adjustment of Prior Distributions to Improve Bayesian Reliability Analysis for Beta-Binomial Data
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Robot PID Control Using Reinforcement Learning
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Expedited Online Learning With Spatial Side Information
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Dual Control of Exploration and Exploitation for Auto-Optimization Control With Active Learning
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Simulation Optimization of Spatiotemporal Dynamics in 3D Geometries
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Bayesian Reinforcement Learning and Bayesian Deep Learning for Blockchains With Mobile Edge Computing
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Hybrid Learning: When Centralized Learning Meets Federated Learning in the Mobile Edge Computing Systems
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Knowledge and Data Guided Hybrid Learning Controller and Its Application to Uncertain Manipulators
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MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control
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Learning-Based Mobile Edge Computing Resource Management to Support Public Blockchain Networks
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Auction-and-Learning Based Lagrange Coded Computing Model for Privacy-Preserving, Secure, and Resilient Mobile Edge Computing
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Multi-Access Edge Computing for Real-Time Applications With Sporadic DAG Tasks – A Graphical Game Approach
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Automated Anomaly Detection via Curiosity-Guided Search and Self-Imitation Learning
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Cooperative Active Learning-Based Dual Control for Exploration and Exploitation in Autonomous Search
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On the Benefit of Optimal Transport for Curriculum Reinforcement Learning
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Recent Advances in Optimal Transport for Machine Learning
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Efficient Distributed Edge Computing for Dependent Delay-Sensitive Tasks in Multi-Operator Multi-Access Networks
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Applications of Physics-Informed Neural Networks in Power Systems - A Review
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Partially Observable Markov Decision Processes in Robotics: A Survey
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Experimental Demonstration of Stochastic Bayesian Inference Using Müller C-Elements
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Cooperative Learning for Spectrum Management in Railway Cognitive Radio Network
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Fast and Secure Computational Offloading With Lagrange Coded Mobile Edge Computing
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Optimal Treatment Regimes: A Review and Empirical Comparison
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Monetary feedback modulates performance and electrophysiological indices of belief updating in reward learning
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Single Trajectory Learning: Exploration Versus Exploitation
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Dynamic Pricing Under Uncertainty in Automotive Services Using Reinforcement Learning and Temporal Convolutional Networks
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Human-Inspired Meta-Reinforcement Learning Using Bayesian Knowledge and Enhanced Deep Q-Network
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A Survey on Bayesian Nonparametric Learning
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AI-Based Discrete-Event Simulations for Manufacturing Schedule Optimization
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Bayesian Strategy Networks Based Soft Actor-Critic Learning
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Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning
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Partially Observable Markov Decision Processes and Robotics
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Q-Learning: Theory and Applications
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Active Inference: Demystified and Compared
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Active Inference and Intentional Behavior
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Bayesian Reinforcement Learning With Limited Cognitive Load
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Synchronization control strategy in multi-layer and multi-axis systems based on the combine cross coupling error
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Survey of imitation learning： tradition and new advances
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Nonparametric additive value functions: Interpretable reinforcement learning with an application to surgical recovery
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Exploration and Incentives in Reinforcement Learning
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A Tutorial on Meta-Reinforcement Learning
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Model-based Reinforcement Learning: A Survey
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Reinforcement Learning, Bit by Bit
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A Tutorial on Thompson Sampling
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Model-Driven Analysis of ECG Using Reinforcement Learning
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Bayesian Advantage Actor Critic for Few Sample Learning using Factor Graph Bayesian Neural Networks
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UAMDP: Uncertainty-Aware Markov Decision Process for Risk Constrained Reinforcement Learning from Probabilistic Forecasts
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Integrating Robust Satisficing, Directed Learning, and Real Options for Resilient Environmental Decisions under Deep Uncertainty
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Beyond Cumulative Returns via Reinforcement Learning over State-Action Occupancy Measures
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