- (book) Dynamic Programming, Bellman R., 1957.
- (book) Dynamic Programming and Optimal Control, Volumes 1 and 2, Bertsekas D., 1995.
- (book) Markov Decision Processes - Discrete Stochastic Dynamic Programming, Puterman M., 1995.
ExpectiMinimax
Optimal strategy in games with chance nodes, Melkó E., Nagy B., 2007.Sparse sampling
A sparse sampling algorithm for near-optimal planning in large Markov decision processes, Kearns M. et al, 2002.MCTS
Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search, Rémi Coulom, SequeL, 2006.UCT
Bandit based Monte-Carlo Planning, Kocsis L., Szepesvári C., 2006.AlphaGo
Mastering the game of Go with deep neural networks and tree search, Silver D. et al, 2016.AlphaGo Zero
Mastering the game of Go without human knowledge, Silver D. et al, 2017.AlphaZero
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm, Silver D. et al, 2017.TrailBlazer
Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning, Grill J. B., Valko M., Munos R., 2017.MCTSnets
Learning to search with MCTSnets, Guez A. et al, 2018.ADI
Solving the Rubik's Cube Without Human Knowledge, McAleer S. et al, 2018.
- (book) Constrained Control and Estimation, Goodwin G., 2005.
PI²
A Generalized Path Integral Control Approach to Reinforcement Learning, Theodorou E. et al, 2010.PI²-CMA
Path Integral Policy Improvement with Covariance Matrix Adaptation, Stulp F., Sigaud O., 2010.iLQG
A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems, Todorov E., 2005.iLQG+
Synthesis and stabilization of complex behaviors through online trajectory optimization, Tassa Y., 2012.
- (book) Model Predictive Control, Camacho E., 1995.
- (book) Predictive Control With Constraints, Maciejowski J. M., 2002.
- Linear Model Predictive Control for Lane Keeping and Obstacle Avoidance on Low Curvature Roads, Turri V. et al, 2013.
MPCC
Optimization-based autonomous racing of 1:43 scale RC cars, Liniger A. et al, 2014. (video 1 | 2)MIQP
Optimal trajectory planning for autonomous driving integrating logical constraints: An MIQP perspective, Qian X., Altché F., Bender P., Stiller C. de La Fortelle A., 2016.
- Minimax analysis of stochastic problems, Shapiro A., Kleywegt A., 2002.
Robust DP
Robust Dynamic Programming, Iyengar G., 2005.- Robust Planning and Optimization, Laumanns M., 2011. (lecture notes)
- Robust Markov Decision Processes, Wiesemann W., Kuhn D., Rustem B., 2012.
Coarse-Id
On the Sample Complexity of the Linear Quadratic Regulator, Dean S., Mania H., Matni N., Recht B., Tu S., 2017.Tube-MPPI
Robust Sampling Based Model Predictive Control with Sparse Objective Information, Williams G. et al, 2018. (video)
- A Comprehensive Survey on Safe Reinforcement Learning, García J., Fernández F., 2015.
ICS
Will the Driver Seat Ever Be Empty?, Fraichard T., 2014.RSS
On a Formal Model of Safe and Scalable Self-driving Cars, Shalev-Shwartz S. et al, 2017.BFTQ
Safe Transfer across Reinforcement Learning Tasks, Carrara N. et al, 2018.
- Simulation of Controlled Uncertain Nonlinear Systems, Tibken B. Hofer E., 1995.
- Trajectory computation of dynamic uncertain systems, Adrot O. Flaus J-M., 2002.
- Simulation of Uncertain Dynamic Systems Described By Interval Models: a Survey, Puig V. et al, 2005.
- Design of interval observers for uncertain dynamical systems, Efimov D., Raïssi T., 2016.
LUCB
PAC Subset Selection in Stochastic Multi-armed Bandits, Kalyanakrishnan S. et al, 2012.Track-and-Stop
Optimal Best Arm Identification with Fixed Confidence, Garivier A., Kaufmann E., 2016.M-LUCB/M-Racing
Maximin Action Identification: A New Bandit Framework for Games, Garivier A., Kaufmann E., Koolen W., 2016.LUCB-micro
Structured Best Arm Identification with Fixed Confidence, Huang R. et al, 2017.
- Reinforcement learning: A survey, Kaelbling L. et al, 1996.
DQN
Playing Atari with Deep Reinforcement Learning, Mnih V. et al, 2013. (video)DDQN
Deep Reinforcement Learning with Double Q-learning, van Hasselt H. Silver D. et al, 2015.DDDQN
Dueling Network Architectures for Deep Reinforcement Learning, Wang Z. et al, 2015. (video)PDDDQN
Prioritized Experience Replay, Schaul T. et al, 2015.NAF
Continuous Deep Q-Learning with Model-based Acceleration, Gu S. et al, 2016.Rainbow
Rainbow: Combining Improvements in Deep Reinforcement Learning, Hessel M. et al, 2017.Ape-X DQfD
Observe and Look Further: Achieving Consistent Performance on Atari, Pohlen T. et al, 2018. (videos)
REINFORCE
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning, Williams R., 1992.Natural Gradient
A Natural Policy Gradient, Kakade S., 2002.- Policy Gradient Methods for Robotics, Peters J., Schaal S., 2006.
TRPO
Trust Region Policy Optimization, Schulman J. et al, 2015. (video)PPO
Proximal Policy Optimization Algorithms, Schulman J. et al, 2017. (video)DPPO
Emergence of Locomotion Behaviours in Rich Environments, Heess N. et al, 2017. (video)
AC
Policy Gradient Methods for Reinforcement Learning with Function Approximation, Sutton R. et al, 1999.NAC
Natural Actor-Critic, Peters J. et al, 2005.DPG
Deterministic Policy Gradient Algorithms, Silver D. et al, 2014.DDPG
Continuous Control With Deep Reinforcement Learning, Lillicrap T. et al, 2015. (video 1 | 2 | 3 | 4)A3C
Asynchronous Methods for Deep Reinforcement Learning, Mnih V. et al 2016. (video 1 | 2 | 3)SAC
Soft Actor-Critic : Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor, Haarnoja T. et al, 2018. (video)
CEM
Learning Tetris Using the Noisy Cross-Entropy Method, Szita I. Lörincz A., 2006. (video)CMAES
Completely Derandomized Self-Adaptation in Evolution Strategies, Hansen N. Ostermeier A., 2001.NEAT
Evolving Neural Networks through Augmenting Topologies, Stanley K., 2002. (video)
Dyna
Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming, Sutton R., 1990.UCRL2
Near-optimal Regret Bounds for Reinforcement Learning, Jaksch T., 2010.PILCO
PILCO: A Model-Based and Data-Efficient Approach to Policy Search, Deisenroth M., Rasmussen C., 2011. (talk)DBN
Probabilistic MDP-behavior planning for cars, Brechtel S. et al, 2011.GPS
End-to-End Training of Deep Visuomotor Policies, Levine S. et al, 2015. (video)DeepMPC
DeepMPC: Learning Deep Latent Features for Model Predictive Control, Lenz I. et al, 2015. (video)SVG
Learning Continuous Control Policies by Stochastic Value Gradients, Heess N. et al, 2015. (video)- Optimal control with learned local models: Application to dexterous manipulation, Kumar V. et al, 2016. (video)
BPTT
Long-term Planning by Short-term Prediction, Shalev-Shwartz S. et al, 2016. (video 1 | 2)- Deep visual foresight for planning robot motion, Finn C., Levine S., 2016. (video)
VIN
Value Iteration Networks, Tamar A. et al , 2016. (video)VPN
Value Prediction Network, Oh J. et al, 2017.- An LSTM Network for Highway Trajectory Prediction, Altché F., de La Fortelle A., 2017.
DistGBP
Model-Based Planning with Discrete and Continuous Actions, Henaff M. et al, 2017. (video 1 | 2)- Prediction and Control with Temporal Segment Models, Mishra N. et al, 2017.
Predictron
The Predictron: End-To-End Learning and Planning, Silver D. et al, 2017. (video)MPPI
Information Theoretic MPC for Model-Based Reinforcement Learning, Williams G. et al, 2017. (video)- Learning Real-World Robot Policies by Dreaming, Piergiovanni A. et al, 2018.
- Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning, Sutton R. et al, 1999.
- Intrinsically motivated learning of hierarchical collections of skills, Barto A. et al, 2004.
- Learning and Transfer of Modulated Locomotor Controllers, Heess N. et al, 2016. (video)
- Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving, Shalev-Shwartz S. et al, 2016.
FuNs
FeUdal Networks for Hierarchical Reinforcement Learning, Vezhnevets A. et al, 2017.- Combining Neural Networks and Tree Search for Task and Motion Planning in Challenging Environments, Paxton C. et al, 2017. (video)
PBVI
Point-based Value Iteration: An anytime algorithm for POMDPs, Pineau J. et al, 2003.cPBVI
Point-Based Value Iteration for Continuous POMDPs, Porta J. et al, 2006.POMCP
Monte-Carlo Planning in Large POMDPs, Silver D., Veness J., 2010.- A POMDP Approach to Robot Motion Planning under Uncertainty, Du Y. et al, 2010.
- Solving Continuous POMDPs: Value Iteration with Incremental Learning of an Efficient Space Representation, Brechtel S. et al, 2013.
- Probabilistic Decision-Making under Uncertainty for Autonomous Driving using Continuous POMDPs, Brechtel S. et al, 2014.
MOMDP
Intention-Aware Motion Planning, Bandyopadhyay T. et al, 2013.- The value of inferring the internal state of traffic participants for autonomous freeway driving, Sunberg Z. et al, 2017.
- Virtual to Real Reinforcement Learning for Autonomous Driving, Pan X. et al, 2017. (video)
- Sim-to-Real: Learning Agile Locomotion For Quadruped Robots, Tan J. et al, 2018. (video)
ME-TRPO
Model-Ensemble Trust-Region Policy Optimization, Kurutach T. et al, 2018. (video)- Kickstarting Deep Reinforcement Learning, Schmitt S. et al, 2018.
- Learning Dexterous In-Hand Manipulation, OpenAI, 2018. (video)
- Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems, Albrecht S. Stone P., 2017.
MILP
Time-optimal coordination of mobile robots along specified paths, Altché F. et al, 2016. (video)MIQP
An Algorithm for Supervised Driving of Cooperative Semi-Autonomous Vehicles, Altché F. et al, 2017. (video)SA-CADRL
Socially Aware Motion Planning with Deep Reinforcement Learning, Chen Y. et al, 2017. (video)- Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction: Theory and experiment, Galceran E. et al, 2017.
- Online decision-making for scalable autonomous systems, Wray K. et al, 2017.
MAgent
MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence, Zheng L. et al, 2017. (video)- Cooperative Motion Planning for Non-Holonomic Agents with Value Iteration Networks, Rehder E. et al, 2017.
COMA
Counterfactual Multi-Agent Policy Gradients, Foerster J. et al, 2017.FTW
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning, Jaderberg M. et al, 2018. (video)
DeepDriving
DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving, Chen C. et al, 2015. (video)- On the Sample Complexity of End-to-end Training vs. Semantic Abstraction Training, Shalev-Shwartz S. et al, 2016.
VAE-MDN-RNN
World Models, Ha D., Schmidhuber J., 2018.MERLIN
Unsupervised Predictive Memory in a Goal-Directed Agent, Wayne G. et al, 2018. (video 1 | 2 | 3 | 4 | 5 6)
- Is the Bellman residual a bad proxy?, Geist M., Piot B., Pietquin O., 2016.
- Deep Reinforcement Learning that Matters, Henderson P. et al, 2017.
- Automatic Bridge Bidding Using Deep Reinforcement Learning, Yeh C. and Lin H., 2016.
- Shared Autonomy via Deep Reinforcement Learning, Reddy S. et al, 2018. (videos)
- Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review, Levine S., 2018.
DQfD
Learning from Demonstrations for Real World Reinforcement Learning, Hester T. et al, 2017. (videos)UPN
Universal Planning Networks, Srinivas A. et al, 2018. (videos)QMDP-RCNN
Reinforcement Learning via Recurrent Convolutional Neural Networks, Shankar T. et al, 2016. (talk)GAIL
Generative Adversarial Imitation Learning, Ho J., Ermon S., 2016.- From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots, Pfeiffer M. et al, 2017. (video)
Branched
End-to-end Driving via Conditional Imitation Learning, Codevilla F. et al, 2017. (video | talk)DeepMimic
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills, Peng X. B. et al, 2018. (video)
- ALVINN, an autonomous land vehicle in a neural network, Pomerleau D., 1989.
- End to End Learning for Self-Driving Cars, Bojarski M. et al, 2016. (video)
- End-to-end Learning of Driving Models from Large-scale Video Datasets, Xu H., Gao Y. et al, 2016. (video)
- End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies, Eraqi H. et al, 2017.
- Driving Like a Human: Imitation Learning for Path Planning using Convolutional Neural Networks, Rehder E. et al, 2017.
- Imitating Driver Behavior with Generative Adversarial Networks, Kuefler A. et al, 2017.
PS-GAIL
Multi-Agent Imitation Learning for Driving Simulation, Bhattacharyya R. et al, 2018. (video)
Projection
Apprenticeship learning via inverse reinforcement learning, Abbeel P. Ng A. 2004.MMP
Maximum margin planning, Ratliff N. et al, 2006.BIRL
Bayesian inverse reinforcement learning, Ramachandran D. Amir E., 2007.MEIRL
Maximum Entropy Inverse Reinforcement Learning, Ziebart B. et al, 2008.CIOC
Continuous Inverse Optimal Control with Locally Optimal Examples, Levine S., Koltun V., 2012. (video)MEDIRL
Maximum Entropy Deep Inverse Reinforcement Learning, Wulfmeier M., 2015.GCL
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization, Finn C. et al, 2016. (video)RIRL
Repeated Inverse Reinforcement Learning, Amin K. et al, 2017.- Bridging the Gap Between Imitation Learning and Inverse Reinforcement Learning, Piot B. et al, 2017.
- Apprenticeship Learning for Motion Planning, with Application to Parking Lot Navigation, Abbeel P. et al, 2008.
- Navigate like a cabbie: Probabilistic reasoning from observed context-aware behavior, Ziebart B. et al, 2008.
- Planning-based Prediction for Pedestrians, Ziebart B. et al, 2009. (video)
- Learning Driving Styles for Autonomous Vehicles from Demonstration, Kuderer M. et al, 2015.
- Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks, Sharifzadeh S. et al, 2016.
- Watch This: Scalable Cost-Function Learning for Path Planning in Urban Environments, Wulfmeier M., 2016. (video)
- Planning for Autonomous Cars that Leverage Effects on Human Actions, Sadigh D. et al, 2016.
- A Learning-Based Framework for Handling Dilemmas in Urban Automated Driving, Lee S., Seo S., 2017.
Dijkstra
A Note on Two Problems in Connexion with Graphs, Dijkstra E. W., 1959.A*
A Formal Basis for the Heuristic Determination of Minimum Cost Paths , Hart P. et al, 1968.- Planning Long Dynamically-Feasible Maneuvers For Autonomous Vehicles, Likhachev M., Ferguson D., 2008.
- Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame, Werling M., Kammel S., 2010. (video)
- 3D perception and planning for self-driving and cooperative automobiles, Stiller C., Ziegler J., 2012.
- Motion Planning under Uncertainty for On-Road Autonomous Driving, Xu W. et al, 2014.
- Monte Carlo Tree Search for Simulated Car Racing, Fischer J. et al, 2015. (video)
RRT*
Sampling-based Algorithms for Optimal Motion Planning, Karaman S., Frazzoli E., 2011. (video)LQG-MP
LQG-MP: Optimized Path Planning for Robots with Motion Uncertainty and Imperfect State Information, van den Berg J. et al, 2010.- Motion Planning under Uncertainty using Differential Dynamic Programming in Belief Space, van den Berg J. et al, 2011.
- Rapidly-exploring Random Belief Trees for Motion Planning Under Uncertainty, Bry A., Roy N., 2011.
PRM-RL
PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning, Faust A. et al, 2017.
- Trajectory planning for Bertha - A local, continuous method, Ziegler J. et al, 2014.
- Learning Attractor Landscapes for Learning Motor Primitives, Ijspeert A. et al, 2002.
PF
Real-time obstacle avoidance for manipulators and mobile robots, Khatib O., 1986.VFH
The Vector Field Histogram - Fast Obstacle Avoidance For Mobile Robots, Borenstein J., 1991.VFH+
VFH+: Reliable Obstacle Avoidance for Fast Mobile Robots, Ulrich I., Borenstein J., 1998.Velocity Obstacles
Motion planning in dynamic environments using velocity obstacles, Fiorini P., Shillert Z., 1998.
- A Review of Motion Planning Techniques for Automated Vehicles, González D. et al, 2016.
- A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles, Paden B. et al, 2016.
- Autonomous driving in urban environments: Boss and the Urban Challenge, Urmson C. et al, 2008.
- The MIT-Cornell collision and why it happened, Fletcher L. et al, 2008.
- Making bertha drive-an autonomous journey on a historic route, Ziegler J. et al, 2014.