¯_( ͡❛ ͜ʖ ͡❛)_/¯ Just recording papers I read everyday ;)

→ Psychology Perspective on Trust

→ Science Behind Human Trust

→ Inverse Reinforcement Learning

→ Behavioral Economics and Business Models

→ Dynamic Modeling of Trust in Autonomy

→ Human-Autonomy Teaming

→ Human-Robot Collaboration

→ Driving Scenario Identification-Classification

→ Black-Box Validation for Self-Driving

→ Battery Management System for Electric Vehicles

→ Graph-Based Reinforcement Learning

→ Efficient Data Sampling

→ Imitation Learning in Autonomous Driving

→ Offline Reinforcement Learning

→ Knowledge & Dataset Distillation

→ CARLA Autonomous Driving Challenge

→ Deep Reinforcement Learning in Autonomous Driving

→ Multi-Agent Learning

→ Curriculum Learning

→ Evolutionary Training

→ Population Based Training

• {2024-07-14} Measuring Trust (2018)

• {2024-06-03} Neural Correlates and Mechanisms of Trust (Neuroergonomics, 2020)
• {2024-06-02} What Does the Brain Tell Us About Trust and Distrust Evidence from a Functional Neuroimaging Study (JSTOR, 2010)
• {2024-06-01} An Integrative Model of Organizational Trust (The Academy of Management Review, 1995)

• {2024-03-07} Inverse Reinforcement Learning without Reinforcement Learning (PMLR, 2023)
• {2024-01-21} BC-IRL: Learning Generalizable Reward Functions from Demonstrations (ICML, 2023)
• {2024-01-20} f-IRL: Inverse Reinforcement Learning via State Marginal Matching (CoRL, 2020)
• {2023-12-26} Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations (ICML, 2019)
• {2023-12-25} Maximum Margin Planning (ICML, 2006)
• {2023-12-23} Inverse Reinforcement Learning Framework for Transferring Task Sequencing Policies from Humans to Robots in Manufacturing Applications (ICRA, 2023)
• {2023-12-12} Active Preference-Based Learning of Reward Functions (RSS, 2017)
• {2023-12-07} Mixed-Initiative Multiagent Apprenticeship Learning for Human Training of Robot Teams (NeurIPS, 2023)
• {2023-12-02} Sampling-based Inverse Reinforcement Learning Algorithms with Safety Constraints (IROS, 2021)
• {2023-12-01} Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints (NeurIPS, 2019)
• {2023-11-29} Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization (ICML, 2016)
• {2023-11-28} Maximum Entropy Inverse Reinforcement Learning (AAAI, 2008)
• {2023-11-27} Apprenticeship Learning via Inverse Reinforcement Learning (ICML, 2004)
• {2023-11-26} Algorithms for Inverse Reinforcement Learning (ICML, 2000)

• {2024-06-06} Prospect Theory: An Analysis of Decision Under Risk (Econometrica, 1979)

• {2024-06-04} Human Trust in Robots: A Survey on Trust Models and Their Controls/Robotics Applications (Journal of Control Systems, 2023)
• {2024-04-21} Modeling Other Minds: Bayesian Inference Explains Human Choices in Group Decision-Making (Science Advances, 2019)
• {2024-04-19} Continuous Ratings in Discrete Bayesian Reputation Systems (ICTM, 2008)
• {2024-04-18} The Beta Reputation System (Electronic Commerce Conference, 2002)
• {2024-04-17} A Survey of Trust and Reputation Systems for Online Service Provision (Decision Support Systems, 2007)
• {2024-03-29} A Kalman Estimation Model of Human Trust in Supervisory Control of Robotic Swarms (HFES, 2020)
• {2024-03-28} Dynamic Trust Metrics for Peer-to-Peer Systems (DEXA, 2005)
• {2024-03-26} A Bayesian Trust Inference Model for Human-Multi-Robot Teams (IJSR, 2021)
• {2024-01-25} Modeling and Control of Trust in Human and Robot Collaborative Manufacturing (RITAS, 2016)
• {2023-12-06} Enabling Team of Teams: A Trust Inference and Propagation (TIP) Model in Multi-Human Multi-Robot Teams (RSS, 2023)
• {2023-11-30} Models of Trust in Human Control of Swarms With Varied Levels of Autonomy (Transactions on Human-Machine Systems, 2019)
• {2023-11-08} Adaptive Trust Model for Multi-Agent Teaming Based on Reinforcement-Learning-Based Fusion (TETCI, 2023)
• {2023-11-07} Real-Time Trust Prediction in Conditionally Automated Driving Using Physiological Measures (ITS, 2023)
• {2023-11-06} Bayesian Optimization Based Trust Model for Human Multi-Robot Collaborative Motion Tasks in Offroad Environments (IJSR, 2023)
• {2023-11-05} A Unified Bi-Directional Model for Natural and Artificial Trust in Human-Robot Collaboration (RAL, 2021)
• {2023-11-04} Towards a Theory of Longitudinal Trust Calibration in Human-Robot Teams (IJRS, 2019)
• {2023-11-03} OPTIMo: Online Probabilistic Trust Inference Model for Asymmetric Human-Robot Collaborations (HRI, 2015)
• {2023-10-30} A Survey on Trust Evaluation Based on Machine Learning (ACM Computing Surveys, 2020)
• {2023-10-26} Trust Modeling Based on Probabilistic Linguistic Term Sets and the MULTIMOORA Method (Expert Systems With Applications, 2021)
• {2023-10-25} Interpersonal Trust Modelling Through Multi-Agent Reinforcement Learning (Cognitive Systems Research, 2024)
• {2023-10-23} Computational Model of Robot Trust in Human Co-Worker for Physical Human-Robot Collaboration (RAL, 2022)
• {2023-10-11} Multi-Task Trust Transfer for Human-Robot Interaction (IJRR, 2020)
• {2023-10-10} TIP: A Trust Inference and Propagation Model in Multi-Human Multi-Robot Teams (HRI, 2023)
• {2023-10-05} Modeling and Predicting Trust Dynamics in Human-Robot Teaming: A Bayesian Inference Approach (IJSR, 2020)
• {2023-10-03} Reverse Psychology in Trust-Aware Human-Robot Interaction (RAL, 2021)
• {2023-09-08} Trust Dynamics and Verbal Assurances in Human Robot Physical Collaboration (Frontiers in Artificial Intelligence, 2021)
• {2023-08-10} Trust Measurement in Human-Autonomy Teams: Development of a Conceptual Toolkit (ACM Transactions on Human-Robot Interaction, 2022)

• {2024-05-31} The Role of Trust in Human-Robot Interaction (Foundations of trusted autonomy, 2018)
• {2024-03-27} Measurement of Trust in Automation: A Narrative Review and Reference Guide (Frontiers in Psychology, 2021)
• {2023-12-15} Asking Easy Questions: A User-Friendly Approach to Active Reward Learning (PMLR, 2020)
• {2023-12-08} A Survey of Multi-Agent Human-Robot Interaction Systems (RAS, 2023)
• {2023-11-14} Clustering Trust Dynamics in a Human-Robot Sequential Decision-Making Task (RAL, 2022)
• {2023-10-27} A Meta-Analysis of Factors Influencing the Development of Trust in Automation: Implications for Understanding Autonomy in Future Systems (HFES, 2016)
• {2023-09-22} Learning Reward Functions from Diverse Sources of Human Feedback: Optimally Integrating Demonstrations and Preferences (IJRR, 2021)
• {2023-08-24} Who/What Is My Teammate? Team Composition Considerations in Human-AI Teaming (Transactions on Human-Machine Systems, 2021)
• {2023-08-17} Exploration of Teammate Trust and Interaction Dynamics in Human-Autonomy Teaming (Transactions on Human-Machine Systems, 2021)
• {2023-08-16} Discovering Emerging Applications of Multi-Valued Logic: Protocols for Human-Autonomy Teaming (ISMVL, 2023)
• {2023-08-15} Agent Transparency: A Review of Current Theory and Evidence (Transactions on Human-Machine Systems, 2020)
• {2023-08-14} Assessing Communication and Trust in an AI Teammate in a Dynamic Task Environment (ICHMS, 2021)
• {2023-08-13} Human-Automation Teaming: Lessons Learned and Future Directions (HIMI, 2018)
• {2023-08-12} Human–Autonomy Teaming: Definitions, Debates, and Directions (Frontiers, 2021)
• {2023-08-11} Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature (HFES, 2020)

• {2024-06-07} When Humans Aren't Optimal: Robots that Collaborate with Risk-Aware Humans (HRI , 2020)
• {2024-03-15} Human Trust in Robots: A Survey on Trust Models and Their Controls/Robotics Applications (Journal of Control Systems , 2023)
• {2024-01-23} Robot Learning on the Job Human-in-the-Loop Autonomy and Learning During Deployment (RSS, 2023)
• {2023-12-27} Learning Human Objectives from Sequences of Physical Corrections (ICRA, 2021)
• {2023-12-05} An Optimization-Based Human Behavior Modeling and Prediction for Human-Robot Collaborative Disassembly (ACC, 2023)
• {2023-12-04} Reward Shaping for Building Trustworthy Robots in Sequential Human-Robot Interaction (IROS, 2023)
• {2023-12-03} Getting to Know One Another: Calibrating Intent, Capabilities and Trust for Human-Robot Collaboration (IROS, 2020)
• {2023-11-10} Partially Observable Markov Decision Processes in Robotics: A Survey (Transactions on Robotics, 2023)
• {2023-10-22} Robot Trust and Self-Confidence Based Role Arbitration Method for Physical Human-Robot Collaboration (ICRA, 2023)
• {2023-10-09} Trust-Aware Motion Planning for Human-Robot Collaboration under Distribution Temporal Logic Specifications (2023)
• {2023-09-20} Efficient Model Learning from Joint-Action Demonstrations for Human-Robot Collaborative Tasks (HRI, 2015)
• {2023-09-14} Maintaining Efficient Collaboration with Trust-Seeking Robots (IROS, 2016)
• {2023-09-13} Human Modeling in Physical Human-Robot Interaction: A Brief Survey (RAL, 2023)
• {2023-09-12} Efficient and Trustworthy Social Navigation via Explicit and Implicit Robot-Human Communication (Transactions on Robotics, 2020)
• {2023-09-11} Trust-Aware Decision Making for Human-Robot Collaboration: Model Learning and Planning (THRI, 2020)
• {2023-09-07} A Hierarchical Human-Robot Interaction-Planning Framework for Task Allocation in Collaborative Industrial Assembly Processes (RAL, 2017)
• {2023-09-06} Prediction of Human Activity Patterns for Human–Robot Collaborative Assembly Tasks (Transactions on Industrial Informatics, 2019)
• {2023-08-25} What Information Should a Robot Convey? (IROS, 2021)
• {2023-08-23} An Empirical Study of Reward Explanations With Human-Robot Interaction Applications (RAL, 2022)
• {2023-06-29} Human Modeling for Human–Robot Collaboration (IJRR, 2017)
• {2023-06-28} Studying Human Robot Interaction and Its Characteristics (IJSR, 2022)
• {2023-06-27} Planning with Trust for Human-Robot Collaboration (HRI, 2018)

• {2023-03-04} Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios (IV, 2022)
• {2022-10-01} A General Autonomous Driving Planner Adaptive to Scenario Characteristics (ITSS, 2022)
• {2022-09-30} Decision-Oriented Driving Scenario Recognition Based on Unsupervised Learning (CICTP, 2021)

• {2022-12-21} Trustworthy Safety Improvement for Autonomous Driving Using Reinforcement Learning (Transportation Research Part C, 2022)
• {2022-09-24} I Know You Can't See Me: Dynamic Occlusion-Aware Safety Validation of Strategic Planners for Autonomous Vehicles Using Hypergames (ICRA, 2022)
• {2022-09-23} Training Adversarial Agents to Exploit Weaknesses in Deep Control Policies (ICRA, 2020)
• {2022-09-22} Prediction-Based Reachability for Collision Avoidance in Autonomous Driving (ICRA, 2021)
• {2022-09-21} Adaptive Failure Search Using Critical States from Domain Experts (ICRA, 2021)
• {2022-09-20} Analysis of Adaptive Multilevel Splitting Algorithms in an Idealized Case* (ESAIM: Probability and Statistics, 2015)
• {2022-09-19} Generating Adversarial Driving Scenarios in High-Fidelity Simulators (ICRA, 2019)
• {2022-09-18} SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning (PMLR, 2021)
• {2022-09-17} AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles (CVPR, 2021)
• {2022-08-30} DeepTest: Automated Testing of Deep-Neural-Network-Driven Autonomous Cars (ICSE, 2018)
• {2022-08-29} Testing Advanced Driver Assistance Systems Using Multi-Objective Search and Neural Networks (ACM ASE, 2016)
• {2022-08-28} Interpretable Safety Validation for Autonomous Vehicles (ITSC, 2020)
• {2022-08-17} Safe Reinforcement Learning Using Black-Box Reachability Analysis (2022)
• {2022-08-16} Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge (CCCI, 2021)
• {2022-07-28} Adaptive Stress Testing with Reward Augmentation for Autonomous Vehicle Validation (ITSC, 2019)
• {2022-07-14} Adaptive Stress Testing for Autonomous Vehicles (IV, 2018)
• {2022-07-13} Survey on Scenario-Based Safety Assessment of Automated Vehicles (IEEE Access, 2020)
• {2022-07-09} Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation (AAAI, 2008)
• {2022-03-06} A Step Towards Efficient Evaluation of Complex Perception Tasks in Simulation (NeurIPS, 2021)
• {2022-03-05} AV-FUZZER: Finding Safety Violations in Autonomous Driving Systems (ISSRE, 2020)
• {2022-03-04} Improved cross entropy-based importance sampling with a flexible mixture model (Reliability Engineering and System Safety, 2019)
• {2022-03-03} Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation (NeurIPS, 2018)
• {2022-03-02} Importance Sampling in Rare Event Simulation (2009)
• {2022-03-01} Generalized Cross-entropy Methods with Applications to Rare-event Simulation and Optimization (Simulation, 2007)
• {2022-02-28} Detecting Safety Problems of Multi-Sensor Fusion in Autonomous Driving (CoRR, 2021)
• {2022-02-27} Efficient Black-box Assessment of Autonomous Vehicle Safety (CoRR, 2019)
• {2022-02-26} Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems (NeurIPS, 2020)
• {2022-02-25} Cross entropy-based importance sampling using Gaussian densities revisited (Structural Safety, 2019)
• {2022-02-24} Adaptive Multilevel Splitting for Rare Event Analysis (Stochastic Analysis and Applications, 2007)
• {2022-02-23} Testing advanced driver assistance systems using multi-objective search and neural networks (ACM, 2016)
• {2022-02-22} Testing ADAS/AV Algorithms with TrustworthySearch (2020)

• {2023-06-06} Cloud-Based Artificial Intelligence Framework for Battery Management System (Energies, 2023)
• {2023-01-14} Battery lifetime of electric vehicles by novel rainflow-counting algorithm with temperature and C-rate dynamics (Journal of Energy Storage, 2023)
• {2023-01-13} Optimization of Electric Vehicle Charging for Battery Maintenance and Degradation Management (Transactions on Transportation Electrification, 2020)
• {2023-01-11} State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks (Transactions on Vehicular Technology, 2017)
• {2023-01-10} Optimal Multistage Charging of NCA Graphite Lithium Ion Batteries Based on Electrothermal Aging Dynamics (Transactions on Transportation Electrification, 2020)
• {2023-01-09} A Deep Reinforcement Learning Framework for Fast Charging of Li-Ion Batteries (Transactions on Transportation Electrification, 2022)
• {2023-01-03} State of Charge Range (U.S. Patent, 2013)
• {2023-01-02} Towards Long Lifetime Battery: AI-Based Manufacturing and Management (Journal of Automatica Sinica, 2022)
• {2023-01-01} Electric Vehicle Battery Cycle Aging Evaluation in Real-World Daily Driving and Vehicle-to-Grid Services (Transactions on Transportation Electrification, 2018)
• {2022-12-07} A Holistic Aging Model for Li(NiMnCo)O2 Based 18650 Lithium-Ion Batteries (Journal of Power Sources 257, 2014)
• {2022-11-10} Electric Vehicle Charge Optimization Including Effects of Lithium-Ion Battery Degradation (VPPC, 2011)
• {2022-11-08} Designing and Prototyping of Lithium-Ion Charging System Using Multi-Step Constant Current Method (World Electric Vehicle Journal, 2022)
• {2022-11-07} Multi-Stage Constant-Current Charging Protocol for a High-Energy-Density Pouch Cell Based on a 622NCM-Graphite System (RSC, 2019)
• {2022-11-06} Smart EV Charging Profiles to Extend Battery Life (UPEC, 2017)
• {2022-11-05} Accounting for Lithium-Ion Battery Degradation in Electric Vehicle Charging Optimization (Journal of Emerging and Selected Topics in Power Electronics, 2014)
• {2022-11-04} Capacity Fade and Aging Models for Electric Batteries and Optimal Charging Strategy for Electric Vehicles (Energy, 2013)
• {2022-11-03} Review of Fast Charging Strategies for Lithium-Ion Battery Systems and Their Applicability for Battery Electric Vehicles (Journal of Energy Storage, 2021)
• {2022-10-16} Optimal Design of Experiment for Parameter Estimation of a Single Particle Model for Lithiumion Batteries (CDC, 2018)
• {2022-10-15} On Parameter Identification of an Equivalent Circuit Model for Lithium-Ion Batteries (CCTA, 2017)
• {2022-10-14} Genetic Optimization and Experimental Validation of a Test Cycle that Maximizes Parameter Identifiability for a Li-Ion Equivalent-Circuit Battery Model (Journal of Energy Storage, 2015)
• {2022-10-13} Impact of Data Sampling Methods on the Performance of Data-driven Parameter Identification for Lithium ion Batteries (IFAC, 2021)
• {2022-10-12} Design of Experiments Applied to Lithium-Ion Batteries: A Literature Review (Journal of Applied Energy, 2022)
• {2022-10-11} A New Design of Experiment Method for Model Parametrisation of Lithium Ion Battery (Journal of Energy Storage, 2022)
• {2022-08-21} Rapid Failure Mode Classification and Quantification in Batteries: A Deep Learning Modeling Framework (Energy Storage Materials, 2022)
• {2022-08-20} Detecting Electric Vehicle Battery Failure via Dynamic-VAE (2022)
• {2022-08-19} Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review (Journal of Electronics, 2021)
• {2022-08-10} Fault Prognosis of Battery System Based on Accurate Voltage Abnormity Prognosis Using Long Short-Term Memory Neural Networks (Applied Energy, 2019)
• {2022-08-09} A Review of Lithium-Ion Battery Thermal Runaway Modeling and Diagnosis Approaches (Journal of Processes, 2022)
• {2022-08-08} Initiation of Thermal Runaway in Lithium-Ion Cells by Inductive Heating (Journal of Power Sources 454, 2020)
• {2022-08-07} Overview of Batteries and Battery Management for Electric Vehicles (Energy Reports 8, 2022)

• {2022-05-06} Reinforcement Learning and Graph Embedding for Binary Truss Topology Optimization Under Stress and Displacement Constraints (Frontiers Built Environ, 2020)
• {2022-05-05} Sample-Efficient Deep Reinforcement Learning with Directed Associative Graph (China Communications, 2021)
• {2022-05-04} Topological Experience Replay (ICLR, 2022)
• {2022-05-03} Graph-Based State Representation for Deep Reinforcement Learning (2020)
• {2022-05-02} Graph-Enhanced Exploration for Goal-Oriented Reinforcement Learning (ICLR, 2022)
• {2022-05-01} Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning (AAMAS, 2021)
• {2022-03-23} Semi-parametric topological memory for navigation (ICLR, 2018)
• {2022-03-22} Search on the Replay Buffer: Bridging Planning and Reinforcement Learning (NeurIPS, 2019)
• {2022-03-10} Sparse Graphical Memory for Robust Planning (NeurIPS, 2020)

• {2024-04-01} Data Quality in Imitation Learning (NeurIPS, 2023)
• {2023-12-14} Human-in-the-Loop Reinforcement Learning in Continuous-Action Space (Transactions on Neural Networks and Learning Systems, 2023)
• {2023-12-13} Guided Policy Exploration for Markov Decision Processes Using an Uncertainty-Based Value-of-Information Criterion (Transactions on Neural Networks and Learning Systems, 2018)
• {2023-12-09} Human-Guided Robot Behavior Learning: A GAN-Assisted Preference-Based Reinforcement Learning Approach (RAL, 2021)
• {2022-07-08} Making Efficient Use of Demonstrations to Solve Hard Exploration Problems (ICLR, 2019)
• {2022-04-29} Active Mini-Batch Sampling Using Repulsive Point Processes (AAAI, 2019)
• {2022-04-28} Rank Degree: An Efficient Algorithm for Graph Sampling (ASONAM, 2016)
• {2022-04-25} Deterministic Graph Exploration for Efficient Graph Sampling (SNAM, 2017)
• {2022-04-23} Learning by Sampling and Compressing: Efficient Graph Representation Learning with Extremely Limited Annotations (AAAI, 2020)
• {2022-04-22} Efficient Deep Representation Learning by Adaptive Latent Space Sampling (2020)

• {2023-11-17} Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization (ICML, 2022)
• {2022-12-31} VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles (ICRA, 2022)
• {2022-10-17} T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction (ITS, 2020)
• {2022-07-05} High-Level Decision Making for Automated Highway Driving via Behavior Cloning (IV, 2022)
• {2022-07-04} Learning a Decision Module by Imitating Driver's Control Behaviors (CoRL, 2020)
• {2022-04-27} Safe and Computational Efficient Imitation Learning for Autonomous Vehicle Driving (ACC, 2020)
• {2022-04-26} Safe Driving via Expert Guided Policy Optimization (CoRL, 2021)
• {2022-04-05} A Human-Like Agent Based on a Hybrid of Reinforcement and Imitation Learning (IJCNN, 2019)
• {2022-04-04} Continuous Online Learning and New Insights to Online Imitation Learning (NeurIPS, 2019)
• {2022-04-03} HG-DAgger: Interactive Imitation Learning with Human Experts (ICRA, 2019)
• {2022-03-28} Safe Imitation Learning on Real-Life Highway Data for Human-like Autonomous Driving (ITSC, 2021)

• {2023-12-24} Hundreds Guide Millions: Adaptive Offline Reinforcement Learning With Expert Guidance (Transactions on Neural Networks and Learning Systems, 2023)
• {2022-06-18} Optimizing Trajectories for Highway Driving with Offline Reinforcement Learning (2022)
• {2022-04-30} RvS: What is Essential for Offline RL via Supervised Learning? (2021)
• {2022-04-24} Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism (NeurIPS, 2021)
• {2022-04-02} Analysis of Learning Influence of Training Data Selected by Distribution Consistency (Sensors, 2021)
• {2022-04-01} Data Distribution Search to Select Core-Set for Machine Learning (SMA, 2020)
• {2022-03-27} Adversarial Imitation Learning from Incomplete Demonstrations (2019)
• {2022-03-26} IQ-Learn: Inverse soft-Q Learning for Imitation (NeurIPS, 2021)
• {2022-03-25} Critic Regularized Regression (NeurIPS, 2020)
• {2022-03-24} Offline Learning from Demonstrations and Unlabeled Experience (NeurIPS, 2020)
• {2022-03-21} Regularized Behavior Value Estimation (2021)
• {2022-03-20} Conservative Q-Learning for Offline Reinforcement Learning (NeurIPS, 2020)
• {2022-03-19} A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems (2022)
• {2022-03-18} Off-Policy Deep Reinforcement Learning without Exploration (PMLR, 2019)
• {2022-03-17} Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems (2020)
• {2022-03-16} Should I Run Offline Reinforcement Learning or Behavioral Cloning? (NeurIPS, 2021)
• {2022-03-12} D4RL: Datasets for Deep Data-Driven Reinforcement Learning (2021)
• {2022-03-11} Prioritized Level Replay (PMLR, 2021)
• {2022-03-09} Prioritized Experience Replay (ICLR, 2016)
• {2022-03-08} Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning (2022)
• {2022-03-07} Deep Learning on a Data Diet: Finding Important Examples Early in Training (NeurIPS, 2021)

• {2022-03-15} Knowledge Distillation: A Survey (IJCV, 2021)
• {2022-03-14} Knowledge distillation in deep learning and its applications (PeerJ Computer Science, 2021)
• {2022-03-13} Dataset Distillation (2020)

• {2022-10-29} PlanT: Explainable Planning Transformers via Object-Level Representations (CoRL, 2022)
• {2022-10-02} TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving (PAMI, 2022)
• {2022-09-25} Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer (CVPR, 2022)
• {2022-07-10} End-to-End Urban Driving by Imitating a Reinforcement Learning Coach (ICCV, 2021)
• {2022-07-03} Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline (2022)
• {2022-07-02} Learning from All Vehicles (CVPR, 2022)
• {2022-07-01} TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving (CVPR, 2022)
• {2022-02-21} GRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Driving (2021)
• {2022-02-20} Sample Efficient Interactive End-to-End Deep Learning for Self-Driving Cars with Selective Multi-Class Safe Dataset Aggregation (IROS, 2019)
• {2022-02-19} End-To-End Multi-Modal Sensors Fusion System For Urban Automated Driving (NIPS, 2018)
• {2022-02-18} DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving (ICCV, 2015)
• {2022-02-17} DropoutDAgger: A Bayesian Approach to Safe Imitation Learning (2017)
• {2022-02-16} EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning (IROS, 2019)
• {2022-02-15} Exploring Data Aggregation in Policy Learning for Vision-Based Urban Autonomous Driving (CVPR, 2020)
• {2022-02-14} NEAT: Neural Attention Fields for End-to-End Autonomous Driving (ICCV, 2021)
• {2022-02-13} Query-Efficient Imitation Learning for End-to-End Simulated Driving (AAAI, 2017)
• {2022-02-12} CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving (ECCV, 2018)
• {2022-02-11} Learning To Drive From a World on Rails (ICCV, 2021)
• {2022-02-10} End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordances (CVPR, 2020)
• {2022-02-09} Multi-Modal Fusion Transformer for End-to-End Autonomous Driving (CVPR, 2021)
• {2022-02-08} Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision (ICVGIP, 2018)
• {2022-02-07} Learning by Cheating (PMLR, 2020)
• {2022-02-06} Deep Q-learning From Demonstrations (AAAI, 2018)
• {2022-02-05} Exploring the Limitations of Behavior Cloning for Autonomous Driving (ICCV, 2019)
• {2022-02-04} A reduction of imitation learning and structured prediction to no-regret online learning (JMLR, 2011)

• {2023-11-21} Prioritized Experience-Based Reinforcement Learning With Human Guidance for Autonomous Driving (Transactions on Neural Networks and Learning Systems, 2022)
• {2023-11-18} Toward Human-in-the-Loop AI: Enhancing Deep Reinforcement Learning via Real-Time Human Guidance for Autonomous Driving (Engineering, 2023)
• {2023-11-16} Human-Guided Reinforcement Learning With Sim-to-Real Transfer for Autonomous Navigation (Transactions on Pattern Analysis and Machine Intelligence, 2023)
• {2022-09-16} Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment (ITSC, 2019)

• {2022-02-03} Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications (ITC, 2020)
• {2022-02-02} Interaction-aware multi-agent reinforcement learning for mobile agents with individual goals (ICRA, 2019)
• {2022-02-01} A generalized training approach for multiagent learning (ICLR, 2020)
• {2022-01-31} MADRaS : Multi Agent Driving Simulator (JAIR, 2021)
• {2022-01-30} Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning (PMLR, 2017)
• {2022-01-29} Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control (ITS, 2020)
• {2022-01-28} Multi-Agent Deep Reinforcement Learning (2016)
• {2022-01-27} Multi-agent actor-critic for mixed cooperative-competitive environments (NeurIPS, 2017)
• {2022-01-26} DeepRacer: Educational Autonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learning (ICRA, 2019)
• {2022-01-25} Cooperative Multi-Agent Learning: The State of the Art (AAMAS, 2005)

• {2022-01-24} Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning (ICLR, 2020)
• {2022-01-23} Grandmaster level in StarCraft II using multi-agent reinforcement learning (Nature, 2019)
• {2022-01-22} From few to more: Large-scale dynamic multiagent curriculum learning (AAAI, 2020)
• {2022-01-21} Deep Reinforcement Learning for Event-Driven Multi-Agent Decision Processes (ITS, 2019)
• {2022-01-20} Cm3: Cooperative multi-goal multi-stage multi-agent reinforcement learning (ICLR, 2020)
• {2022-01-19} Cooperative Multi-agent Control Using Deep Reinforcement Learning (AAMAS, 2017)
• {2022-01-18} BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning (ICRA, 2019)
• {2022-01-17} A survey on transfer learning for multiagent reinforcement learning systems (JAIR, 2019)
• {2022-01-16} Curriculum learning: A survey (2021)
• {2022-01-15} Behaviorally Diverse Traffic Simulation via Reinforcement Learning (IROS, 2020)

• {2022-01-14} A Coevolutionary Approach to Deep Multi-Agent Reinforcement Learning (GECCO, 2021)
• {2022-01-13} Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination (PMLR, 2020)
• {2022-01-12} Genetic Algorithms, Tournament Selection, and the Effects of Noise (Complex systems, 1995)
• {2022-01-11} Evolution Strategies as a Scalable Alternative to Reinforcement Learning (NeurIPS, 2017)
• {2022-01-10} Evolution-Guided Policy Gradient in Reinforcement Learning (NeurIPS, 2018)
• {2022-01-09} Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation (IEEE Access, 2020)
• {2022-01-08} Evolutionary Algorithms for Reinforcement Learning (JAIR, 1999)
• {2022-01-07} Competitive Evolution Multi-Agent Deep Reinforcement Learning (ACM, 2019)
• {2022-01-06} Collaborative Evolutionary Reinforcement Learning (PMLR, 2019)

• {2022-01-05} Population Based Training of Neural Networks (2017)
• {2022-01-04} Two-Stage Population Based Training Method for Deep Reinforcement Learning (ACM, 2019)
• {2022-01-03} Human-level performance in 3D multiplayer games with population-based reinforcement learning (Science, 2019)
• {2022-01-02} Evolutionary population curriculum for scaling multi-agent reinforcement learning (ICLR, 2020)
• {2022-01-01} A Generalized Framework for Population Based Training (ACM, 2019)