Table of contents

• Optimal Control
  • Dynamic Programming
  • Linear Programming
  • Tree-Based Planning
  • Control Theory
  • Model Predictive Control
• Safe Control
  • Robust Control
  • Risk-Averse Control
  • Value-Constrained Control
  • State-Constrained Control and Stability
  • Uncertain Dynamical Systems
• Game Theory
• Sequential Learning
  • Multi-Armed Bandit
    • Best Arm Identification
    • Black-box Optimization
  • Reinforcement Learning
    • Theory
    • Value-based
    • Policy-based
      • Policy Gradient
      • Actor-critic
      • Derivative-free
    • Model-based
    • Exploration
    • Hierarchy and Temporal Abstraction
    • Partial Observability
    • Transfer
    • Multi-agent
    • Representation Learning
    • Offline
• Learning from Demonstrations
  • Imitation Learning
    • Applications to Autonomous Driving
  • Inverse Reinforcement Learning
    • Applications to Autonomous Driving
• Motion Planning
  • Search
  • Sampling
  • Optimization
  • Reactive
  • Architecture and applications

Optimal Control:

Dynamic Programming

• (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).
• An Upper Bound on the Loss from Approximate Optimal-Value Functions, Singh S., Yee R. (1994).
• Stochastic optimization of sailing trajectories in an upwind regatta, Dalang R. et al. (2015).

Linear Programming

• (book) Markov Decision Processes - Discrete Stochastic Dynamic Programming, Puterman M. (1995).
• REPS Relative Entropy Policy Search, Peters J. et al. (2010).

Tree-Based Planning

• 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).
• Bandit Algorithms for Tree Search, Coquelin P-A., Munos R. (2007).
• OPD Optimistic Planning for Deterministic Systems, Hren J., Munos R. (2008).
• OLOP Open Loop Optimistic Planning, Bubeck S., Munos R. (2010).
• SOOP Optimistic Planning for Continuous-Action Deterministic Systems, Buşoniu L. et al. (2011).
• OPSS Optimistic planning for sparsely stochastic systems, L. Buşoniu, R. Munos, B. De Schutter, and R. Babuska (2011).
• HOOT Sample-Based Planning for Continuous ActionMarkov Decision Processes, Mansley C., Weinstein A., Littman M. (2011).
• HOLOP Bandit-Based Planning and Learning inContinuous-Action Markov Decision Processes, Weinstein A., Littman M. (2012).
• BRUE Simple Regret Optimization in Online Planning for Markov Decision Processes, Feldman Z. and Domshlak C. (2014).
• LGP Logic-Geometric Programming: An Optimization-Based Approach to Combined Task and Motion Planning, Toussaint M. (2015). 🎞️
• 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).
• OPC/SOPC Continuous-action planning for discounted infinite-horizon nonlinear optimal control with Lipschitz values, Buşoniu L., Pall E., Munos R. (2018).
• Real-time tree search with pessimistic scenarios: Winning the NeurIPS 2018 Pommerman Competition, Osogami T., Takahashi T. (2019)

Control Theory

• (book) The Mathematical Theory of Optimal Processes, L. S. Pontryagin, Boltyanskii V. G., Gamkrelidze R. V., and Mishchenko E. F. (1962).
• (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). :octocat:
• iLQG+ Synthesis and stabilization of complex behaviors through online trajectory optimization, Tassa Y. (2012).

Model Predictive Control

• (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). 🎞️ | 🎞️
• 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).

Safe Control :

Robust Control

• 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).
• Safe and Robust Learning Control with Gaussian Processes, Berkenkamp F., Schoellig A. (2015). 🎞️
• Tube-MPPI Robust Sampling Based Model Predictive Control with Sparse Objective Information, Williams G. et al. (2018). 🎞️
• Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning, Lukas Bronke et al. (2021). :octocat:

Risk-Averse Control

• A Comprehensive Survey on Safe Reinforcement Learning, García J., Fernández F. (2015).
• RA-QMDP Risk-averse Behavior Planning for Autonomous Driving under Uncertainty, Naghshvar M. et al. (2018).
• StoROO X-Armed Bandits: Optimizing Quantiles and Other Risks, Torossian L., Garivier A., Picheny V. (2019).
• Worst Cases Policy Gradients, Tang Y. C. et al. (2019).
• Model-Free Risk-Sensitive Reinforcement Learning, Delétang G. et al. (2021).
• Optimal Thompson Sampling strategies for support-aware CVaR bandits, Baudry D., Gautron R., Kaufmann E., Maillard O. (2021).

Value-Constrained Control

• ICS Will the Driver Seat Ever Be Empty?, Fraichard T. (2014).
• SafeOPT Safe Controller Optimization for Quadrotors with Gaussian Processes, Berkenkamp F., Schoellig A., Krause A. (2015). 🎞️ :octocat:
• SafeMDP Safe Exploration in Finite Markov Decision Processes with Gaussian Processes, Turchetta M., Berkenkamp F., Krause A. (2016). :octocat:
• RSS On a Formal Model of Safe and Scalable Self-driving Cars, Shalev-Shwartz S. et al. (2017).
• CPO Constrained Policy Optimization, Achiam J., Held D., Tamar A., Abbeel P. (2017). :octocat:
• RCPO Reward Constrained Policy Optimization, Tessler C., Mankowitz D., Mannor S. (2018).
• BFTQ A Fitted-Q Algorithm for Budgeted MDPs, Carrara N. et al. (2018).
• SafeMPC Learning-based Model Predictive Control for Safe Exploration, Koller T, Berkenkamp F., Turchetta M. Krause A. (2018).
• CCE Constrained Cross-Entropy Method for Safe Reinforcement Learning, Wen M., Topcu U. (2018). :octocat:
• LTL-RL Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving, Bouton M. et al. (2019).
• Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments, Bouton M. et al. (2019). :octocat:
• Batch Policy Learning under Constraints, Le H., Voloshin C., Yue Y. (2019).
• Value constrained model-free continuous control, Bohez S. et al (2019). 🎞️
• Safely Learning to Control the Constrained Linear Quadratic Regulator, Dean S. et al (2019).
• Learning to Walk in the Real World with Minimal Human Effort, Ha S. et al. (2020) 🎞️
• Responsive Safety in Reinforcement Learning by PID Lagrangian Methods, Stooke A., Achiam J., Abbeel P. (2020). :octocat:
• Envelope MOQ-Learning A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation, Yang R. et al (2019).

State-Constrained Control and Stability

• HJI-reachability Safe learning for control: Combining disturbance estimation, reachability analysis and reinforcement learning with systematic exploration, Heidenreich C. (2017).
• MPC-HJI On Infusing Reachability-Based Safety Assurance within Probabilistic Planning Frameworks for Human-Robot Vehicle Interactions, Leung K. et al. (2018).
• A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems, Fisac J. et al (2017). 🎞️
• Safe Model-based Reinforcement Learning with Stability Guarantees, Berkenkamp F. et al. (2017).
• Lyapunov-Net Safe Interactive Model-Based Learning, Gallieri M. et al. (2019).
• Enforcing robust control guarantees within neural network policies, Donti P. et al. (2021). :octocat:
• ATACOM Robot Reinforcement Learning on the Constraint Manifold, Liu P. et al (2021).

Uncertain Dynamical Systems

• 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).

Game Theory:

• Hierarchical Game-Theoretic Planning for Autonomous Vehicles, Fisac J. et al. (2018).
• Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Games, Fridovich-Keil D. et al. (2019). 🎞️

Sequential Learning:

• Prediction, Learning and Games, Cesa-Bianchi N., Lugosi G. (2006).

Multi-Armed Bandit:

• TS On the Likelihood that One Unknown Probability Exceeds Another in View of the Evidence of Two Samples, Thompson W. (1933).
• Exploration and Exploitation in Organizational Learning, March J. (1991).
• UCB1 / UCB2 Finite-time Analysis of the Multiarmed Bandit Problem, Auer P., Cesa-Bianchi N., Fischer P. (2002).
• Empirical Bernstein / UCB-V Exploration-exploitation tradeoff using variance estimates in multi-armed bandits, Audibert J-Y, Munos R., Szepesvari C. (2009).
• Empirical Bernstein Bounds and Sample Variance Penalization, Maurer A., Ponti M. (2009).
• An Empirical Evaluation of Thompson Sampling, Chapelle O., Li L. (2011).
• kl-UCB The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond, Garivier A., Cappé O. (2011).
• KL-UCB Kullback-Leibler Upper Confidence Bounds for Optimal Sequential Allocation, Cappé O. et al. (2013).
• IDS Information Directed Sampling and Bandits with Heteroscedastic Noise Kirschner J., Krause A. (2018).

Contextual

• LinUCB A Contextual-Bandit Approach to Personalized News Article Recommendation, Li L. et al. (2010).
• OFUL Improved Algorithms for Linear Stochastic Bandits, Abbasi-yadkori Y., Pal D., Szepesvári C. (2011).
• Contextual Bandits with Linear Payoff Functions, Chu W. et al. (2011).
• Self-normalization techniques for streaming confident regression, Maillard O.-A. (2017).
• Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems Mann T. et al. (2018). (prediction setting)
• Weighted Linear Bandits for Non-Stationary Environments, Russac Y. et al. (2019).
• Linear bandits with Stochastic Delayed Feedback, Vernade C. et al. (2020).

Best Arm Identification:

• Successive Elimination Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems, Even-Dar E. et al. (2006).
• LUCB PAC Subset Selection in Stochastic Multi-armed Bandits, Kalyanakrishnan S. et al. (2012).
• UGapE Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence, Gabillon V., Ghavamzadeh M., Lazaric A. (2012).
• Sequential Halving Almost Optimal Exploration in Multi-Armed Bandits, Karnin Z. et al (2013).
• M-LUCB / M-Racing Maximin Action Identification: A New Bandit Framework for Games, Garivier A., Kaufmann E., Koolen W. (2016).
• Track-and-Stop Optimal Best Arm Identification with Fixed Confidence, Garivier A., Kaufmann E. (2016).
• LUCB-micro Structured Best Arm Identification with Fixed Confidence, Huang R. et al. (2017).

Black-box Optimization:

• GP-UCB Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design, Srinivas N., Krause A., Kakade S., Seeger M. (2009).
• HOO X–Armed Bandits, Bubeck S., Munos R., Stoltz G., Szepesvari C. (2009).
• DOO/SOO Optimistic Optimization of a Deterministic Function without the Knowledge of its Smoothness, Munos R. (2011).
• StoOO From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning, Munos R. (2014).
• StoSOO Stochastic Simultaneous Optimistic Optimization, Valko M., Carpentier A., Munos R. (2013).
• POO Black-box optimization of noisy functions with unknown smoothness, Grill J-B., Valko M., Munos R. (2015).
• EI-GP Bayesian Optimization in AlphaGo, Chen Y. et al. (2018)

Reinforcement Learning:

• Reinforcement learning: A survey, Kaelbling L. et al. (1996).

Theory:

• Expected mistake bound model for on-line reinforcement learning, Fiechter C-N. (1997).
• UCRL2 Near-optimal Regret Bounds for Reinforcement Learning, Jaksch T. (2010).
• PSRL Why is Posterior Sampling Better than Optimism for Reinforcement Learning?, Osband I., Van Roy B. (2016).
• UCBVI Minimax Regret Bounds for Reinforcement Learning, Azar M., Osband I., Munos R. (2017).
• Q-Learning-UCB Is Q-Learning Provably Efficient?, Jin C., Allen-Zhu Z., Bubeck S., Jordan M. (2018).
• LSVI-UCB Provably Efficient Reinforcement Learning with Linear Function Approximation, Jin C., Yang Z., Wang Z., Jordan M. (2019).
• Lipschitz Continuity in Model-based Reinforcement Learning, Asadi K. et al (2018).
• On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces, Yang Z., Jin C., Wang Z., Wang M., Jordan M. (2021)

Generative Model

• QVI On the Sample Complexity of Reinforcement Learning with a Generative Model, Azar M., Munos R., Kappen B. (2012).
• Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal, Agarwal A. et al. (2019).

Policy Gradient

• Policy Gradient Methods for Reinforcement Learning with Function Approximation, Sutton R. et al (2000).
• Approximately Optimal Approximate Reinforcement Learning, Kakade S., Langford J. (2002).
• On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift, Agarwal A. et al. (2019)
• PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning, Agarwal A. et al. (2020)
• Is the Policy Gradient a Gradient?, Nota C., Thomas P. S. (2020).

Linear Systems

• PAC Adaptive Control of Linear Systems, Fiechter C.-N. (1997)
• OFU-LQ Regret Bounds for the Adaptive Control of Linear Quadratic Systems, Abbasi-Yadkori Y., Szepesvari C. (2011).
• TS-LQ Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems, Abeille M., Lazaric A. (2018).
• Exploration-Exploitation with Thompson Sampling in Linear Systems, Abeille M. (2017). (phd thesis)
• Coarse-Id On the Sample Complexity of the Linear Quadratic Regulator, Dean S., Mania H., Matni N., Recht B., Tu S. (2017).
• Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator, Dean S. et al (2018).
• Robust exploration in linear quadratic reinforcement learning, Umenberger J. et al (2019).
• Online Control with Adversarial Disturbances, Agarwal N. et al (2019).
• Logarithmic Regret for Online Control, Agarwal N. et al (2019).

Value-based:

• NFQ Neural fitted Q iteration - First experiences with a data efficient neural Reinforcement Learning method, Riedmiller M. (2005).
• DQN Playing Atari with Deep Reinforcement Learning, Mnih V. et al. (2013). 🎞️
• 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). 🎞️
• 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). 🎞️

Policy-based:

Policy gradient

• 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). 🎞️
• PPO Proximal Policy Optimization Algorithms, Schulman J. et al. (2017). 🎞️
• DPPO Emergence of Locomotion Behaviours in Rich Environments, Heess N. et al. (2017). 🎞️

Actor-critic

• 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). 🎞️ 1 | 2 | 3 | 4
• MACE Terrain-Adaptive Locomotion Skills Using Deep Reinforcement Learning, Peng X., Berseth G., van de Panne M. (2016). 🎞️ | 🎞️
• A3C Asynchronous Methods for Deep Reinforcement Learning, Mnih V. et al 2016. 🎞️ 1 | 2 | 3
• SAC Soft Actor-Critic : Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor, Haarnoja T. et al. (2018). 🎞️
• MPO Maximum a Posteriori Policy Optimisation, Abdolmaleki A. et al (2018).
• A Deeper Look at Discounting Mismatch in Actor-Critic Algorithms, Zhang S., Laroche R. et al. (2020).

Derivative-free

• CEM Learning Tetris Using the Noisy Cross-Entropy Method, Szita I., Lörincz A. (2006). 🎞️
• CMAES Completely Derandomized Self-Adaptation in Evolution Strategies, Hansen N., Ostermeier A. (2001).
• NEAT Evolving Neural Networks through Augmenting Topologies, Stanley K. (2002). 🎞️
• iCEM Sample-efficient Cross-Entropy Method for Real-time Planning, Pinneri C. et al. (2020).

Model-based:

• Dyna Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming, Sutton R. (1990).
• 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). 🎞️
• DeepMPC DeepMPC: Learning Deep Latent Features for Model Predictive Control, Lenz I. et al. (2015). 🎞️
• SVG Learning Continuous Control Policies by Stochastic Value Gradients, Heess N. et al. (2015). 🎞️
• FARNN Nonlinear Systems Identification Using Deep Dynamic Neural Networks, Ogunmolu O. et al. (2016). :octocat:
• Optimal control with learned local models: Application to dexterous manipulation, Kumar V. et al. (2016). 🎞️
• BPTT Long-term Planning by Short-term Prediction, Shalev-Shwartz S. et al. (2016). 🎞️ 1 | 2
• Deep visual foresight for planning robot motion, Finn C., Levine S. (2016). 🎞️
• VIN Value Iteration Networks, Tamar A. et al (2016). 🎞️
• VPN Value Prediction Network, Oh J. et al. (2017).
• DistGBP Model-Based Planning with Discrete and Continuous Actions, Henaff M. et al. (2017). 🎞️ 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). 🎞️
• MPPI Information Theoretic MPC for Model-Based Reinforcement Learning, Williams G. et al. (2017). :octocat: 🎞️
• Learning Real-World Robot Policies by Dreaming, Piergiovanni A. et al. (2018).
• Coupled Longitudinal and Lateral Control of a Vehicle using Deep Learning, Devineau G., Polack P., Alchté F., Moutarde F. (2018) 🎞️
• PlaNet Learning Latent Dynamics for Planning from Pixels, Hafner et al. (2018). 🎞️
• NeuralLander Neural Lander: Stable Drone Landing Control using Learned Dynamics, Shi G. et al. (2018). 🎞️
• DBN+POMCP Towards Human-Like Prediction and Decision-Making for Automated Vehicles in Highway Scenarios , Sierra Gonzalez D. (2019).
• Planning with Goal-Conditioned Policies, Nasiriany S. et al. (2019). 🎞️
• MuZero Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model, Schrittwiese J. et al. (2019). :octocat:
• BADGR BADGR: An Autonomous Self-Supervised Learning-Based Navigation System, Kahn G., Abbeel P., Levine S. (2020). 🎞️ :octocat:
• H-UCRL Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning, Curi S., Berkenkamp F., Krause A. (2020). :octocat:

Exploration:

• Combating Reinforcement Learning’s Sisyphean Curse with Intrinsic Fear, Lipton Z. et al. (2016).
• Pseudo-count Unifying Count-Based Exploration and Intrinsic Motivation, Bellemare M. et al (2016). 🎞️
• HER Hindsight Experience Replay, Andrychowicz M. et al. (2017). 🎞️
• VHER Visual Hindsight Experience Replay, Sahni H. et al. (2019).
• RND Exploration by Random Network Distillation, Burda Y. et al. (OpenAI) (2018). 🎞️
• Go-Explore Go-Explore: a New Approach for Hard-Exploration Problems, Ecoffet A. et al. (Uber) (2018). 🎞️
• C51-IDS Information-Directed Exploration for Deep Reinforcement Learning, Nikolov N., Kirschner J., Berkenkamp F., Krause A. (2019). :octocat:
• Plan2Explore Planning to Explore via Self-Supervised World Models, Sekar R. et al. (2020). 🎞️ :octocat:
• RIDE RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments, Raileanu R., Rocktäschel T., (2020). :octocat:

Hierarchy and Temporal Abstraction:

• 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).
• OC The Option-Critic Architecture, Bacon P-L., Harb J., Precup D. (2016).
• Learning and Transfer of Modulated Locomotor Controllers, Heess N. et al. (2016). 🎞️
• 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). 🎞️
• DeepLoco DeepLoco: Dynamic Locomotion Skills Using Hierarchical Deep Reinforcement Learning , Peng X. et al. (2017). 🎞️ | 🎞️
• Hierarchical Policy Design for Sample-Efficient Learning of Robot Table Tennis Through Self-Play, Mahjourian R. et al (2018). 🎞️
• DAC DAC: The Double Actor-Critic Architecture for Learning Options, Zhang S., Whiteson S. (2019).
• Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real, Nachum O. et al (2019). 🎞️
• SoftCon: Simulation and Control of Soft-Bodied Animals with Biomimetic Actuators, Min S. et al. (2020). 🎞️ :octocat:
• H-REIL Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving, Cao Z. et al. (2020). 🎞️ 1, 2

Partial Observability:

• 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).
• Probabilistic Online POMDP Decision Making for Lane Changes in Fully Automated Driving, Ulbrich S., Maurer M. (2013).
• 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).
• DNC Hybrid computing using a neural network with dynamic external memory, Graves A. et al (2016). 🎞️
• The value of inferring the internal state of traffic participants for autonomous freeway driving, Sunberg Z. et al. (2017).
• Belief State Planning for Autonomously Navigating Urban Intersections, Bouton M., Cosgun A., Kochenderfer M. (2017).
• Scalable Decision Making with Sensor Occlusions for Autonomous Driving, Bouton M. et al. (2018).
• Probabilistic Decision-Making at Road Intersections: Formulation and Quantitative Evaluation, Barbier M., Laugier C., Simonin O., Ibanez J. (2018).
• Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing, Kaufmann E. et al. (2018). 🎞️
• social perception Behavior Planning of Autonomous Cars with Social Perception, Sun L. et al (2019).

Transfer:

• IT&E Robots that can adapt like animals, Cully A., Clune J., Tarapore D., Mouret J-B. (2014). 🎞️
• MAML Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, Finn C., Abbeel P., Levine S. (2017). 🎞️
• Virtual to Real Reinforcement Learning for Autonomous Driving, Pan X. et al. (2017). 🎞️
• Sim-to-Real: Learning Agile Locomotion For Quadruped Robots, Tan J. et al. (2018). 🎞️
• ME-TRPO Model-Ensemble Trust-Region Policy Optimization, Kurutach T. et al. (2018). 🎞️
• Kickstarting Deep Reinforcement Learning, Schmitt S. et al. (2018).
• Learning Dexterous In-Hand Manipulation, OpenAI (2018). 🎞️
• GrBAL / ReBAL Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning, Nagabandi A. et al. (2018). 🎞️
• Learning agile and dynamic motor skills for legged robots, Hwangbo J. et al. (ETH Zurich / Intel ISL) (2019). 🎞️
• Robust Recovery Controller for a Quadrupedal Robot using Deep Reinforcement Learning, Lee J., Hwangbo J., Hutter M. (ETH Zurich RSL) (2019)
• IT&E Learning and adapting quadruped gaits with the “Intelligent Trial & Error” algorithm, Dalin E., Desreumaux P., Mouret J-B. (2019). 🎞️
• FAMLE Fast Online Adaptation in Robotics through Meta-Learning Embeddings of Simulated Priors, Kaushik R., Anne T., Mouret J-B. (2020). 🎞️
• Robust Deep Reinforcement Learning against Adversarial Perturbations on Observations, Zhang H. et al (2020). :octocat:
• Learning quadrupedal locomotion over challenging terrain, Lee J. et al. (2020). 🎞️
• PACOH PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees, Rothfuss J., Fortuin V., Josifoski M., Krause A. (2021).
• Model-Based Domain Generalization, Robey A. et al. (2021).
• SimGAN SimGAN: Hybrid Simulator Identification for Domain Adaptation via Adversarial Reinforcement Learning, Jiang Y. et al. (2021). 🎞️ :octocat:
• Learning robust perceptive locomotion for quadrupedal robots in the wild, Miki T. et al. (2022).

Multi-agent:

• Minimax-Q Markov games as a framework for multi-agent reinforcement learning, M. Littman (1994).
• 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). 🎞️
• MIQP An Algorithm for Supervised Driving of Cooperative Semi-Autonomous Vehicles, Altché F. et al. (2017). 🎞️
• SA-CADRL Socially Aware Motion Planning with Deep Reinforcement Learning, Chen Y. et al. (2017). 🎞️
• 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). 🎞️
• Cooperative Motion Planning for Non-Holonomic Agents with Value Iteration Networks, Rehder E. et al. (2017).
• MPPO Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning, Long P. et al. (2017). 🎞️
• COMA Counterfactual Multi-Agent Policy Gradients, Foerster J. et al. (2017).
• MADDPG Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments, Lowe R. et al (2017). :octocat:
• FTW Human-level performance in first-person multiplayer games with population-based deep reinforcement learning, Jaderberg M. et al. (2018). 🎞️
• Towards Learning Multi-agent Negotiations via Self-Play, Tang Y. C. (2020).
• MAPPO The Surprising Effectiveness of MAPPO in Cooperative, Multi-Agent Games, Yu C. et al. (2021). |:octocat:](https://github.com/marlbenchmark/on-policy)
• Many-agent Reinforcement Learning, Yang Y. (2021)

Representation Learning

• Variable Resolution Discretization in Optimal Control, Munos R., Moore A. (2002). 🎞️
• DeepDriving DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving, Chen C. et al. (2015). 🎞️
• On the Sample Complexity of End-to-end Training vs. Semantic Abstraction Training, Shalev-Shwartz S. et al. (2016).
• Learning sparse representations in reinforcement learning with sparse coding, Le L., Kumaraswamy M., White M. (2017).
• World Models, Ha D., Schmidhuber J. (2018). 🎞️ :octocat:
• Learning to Drive in a Day, Kendall A. et al. (2018). 🎞️
• MERLIN Unsupervised Predictive Memory in a Goal-Directed Agent, Wayne G. et al. (2018). 🎞️ 1 | 2 | 3 | 4 | 5 | 6
• Variational End-to-End Navigation and Localization, Amini A. et al. (2018). 🎞️
• Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks, Lee M. et al. (2018). 🎞️
• Deep Neuroevolution of Recurrent and Discrete World Models, Risi S., Stanley K.O. (2019). 🎞️ :octocat:
• FERM A Framework for Efficient Robotic Manipulation, Zhan A., Zhao R. et al. (2021). :octocat:
• S4RL S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning, Sinha S. et al (2021).

Offline

• SPI-BB Safe Policy Improvement with Baseline Bootstrapping, Laroche R. et al (2019).
• AWAC AWAC: Accelerating Online Reinforcement Learning with Offline Datasets, Nair A. et al (2020).
• CQL Conservative Q-Learning for Offline Reinforcement Learning, Kumar A. et al. (2020).
• Decision Transformer: Reinforcement Learning via Sequence Modeling, Chen L., Lu K. et al. (2021). :octocat:
• Reinforcement Learning as One Big Sequence Modeling Problem, Janner M., Li Q., Levine S. (2021).

Other

• 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). 🎞️
• Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review, Levine S. (2018).
• The Value Function Polytope in Reinforcement Learning, Dadashi R. et al. (2019).
• On Value Functions and the Agent-Environment Boundary, Jiang N. (2019).
• How to Train Your Robot with Deep Reinforcement Learning; Lessons We’ve Learned, Ibartz J. et al (2021).

Learning from Demonstrations:

Imitation Learning

• DAgger A Reduction of Imitation Learning and Structured Predictionto No-Regret Online Learning, Ross S., Gordon G., Bagnell J. A. (2011).
• QMDP-RCNN Reinforcement Learning via Recurrent Convolutional Neural Networks, Shankar T. et al. (2016). (talk)
• DQfD Learning from Demonstrations for Real World Reinforcement Learning, Hester T. et al. (2017). 🎞️
• Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for Urban Autonomy, Barnes D., Maddern W., Posner I. (2016). 🎞️
• 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). 🎞️
• Branched End-to-end Driving via Conditional Imitation Learning, Codevilla F. et al. (2017). 🎞️ | talk
• UPN Universal Planning Networks, Srinivas A. et al. (2018). 🎞️
• DeepMimic DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills, Peng X. B. et al. (2018). 🎞️
• R2P2 Deep Imitative Models for Flexible Inference, Planning, and Control, Rhinehart N. et al. (2018). 🎞️
• Learning Agile Robotic Locomotion Skills by Imitating Animals, Bin Peng X. et al (2020). 🎞️
• Deep Imitative Models for Flexible Inference, Planning, and Control, Rhinehart N., McAllister R., Levine S. (2020).

Applications to Autonomous Driving:

• 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). 🎞️
• End-to-end Learning of Driving Models from Large-scale Video Datasets, Xu H., Gao Y. et al. (2016). 🎞️
• 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). 🎞️ :octocat:
• Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety, Chen J. et al. (2019).

Inverse Reinforcement Learning

• 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).
• LEARCH Learning to search: Functional gradient techniques for imitation learning, Ratliff N., Siver D. Bagnell A. (2009).
• CIOC Continuous Inverse Optimal Control with Locally Optimal Examples, Levine S., Koltun V. (2012). 🎞️
• 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). 🎞️
• 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).

Applications to Autonomous Driving:

• 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). 🎞️
• Learning for autonomous navigation, Bagnell A. et al. (2010).
• Learning Autonomous Driving Styles and Maneuvers from Expert Demonstration, Silver D. et al. (2012).
• 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). 🎞️
• 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).
• Learning Trajectory Prediction with Continuous Inverse Optimal Control via Langevin Sampling of Energy-Based Models, Xu Y. et al. (2019).
• Analyzing the Suitability of Cost Functions for Explaining and Imitating Human Driving Behavior based on Inverse Reinforcement Learning, Naumann M. et al (2020).

Motion Planning:

Search

• 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). 🎞️
• 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). 🎞️

Sampling

• RRT* Sampling-based Algorithms for Optimal Motion Planning, Karaman S., Frazzoli E. (2011). 🎞️
• 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).

Optimization

• 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).
• Online Motion Planning based on Nonlinear Model Predictive Control with Non-Euclidean Rotation Groups, Rösmann C. et al (2020). :octocat:

Reactive

• 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).

Architecture and applications

• 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).