godmethium/daily-paper-reading

Just recording papers I read everyday ;)

daily-paper-reading

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

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Graph Based Reinforcement Learning

• {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)

Data Sampling

• {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)

Imitation Learning in Autonomous Driving

• {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)

Offline Reinforcement Learning

• {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)

Knowledge & Dataset Distillation

• {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)

BlackBox Validation

• {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)

Carla Challenge

• {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)

Multi-Agent Learning

• {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)

Curriculum Learning

• {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)

Evolutionary Training

• {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)

Population Training

• {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)