Abstract
In recent trends, artificial intelligence (AI) is used for the creation of complex automated control systems. Still, researchers are trying to make a completely autonomous system that resembles human beings. Researchers working in AI think that there is a strong connection present between the learning pattern of human and AI. They have analyzed that machine learning (ML) algorithms can effectively make self-learning systems. ML algorithms are a sub-field of AI in which reinforcement learning (RL) is the only available methodology that resembles the learning mechanism of the human brain. Therefore, RL must take a key role in the creation of autonomous robotic systems. In recent years, RL has been applied on many platforms of the robotic systems like an air-based, under-water, land-based, etc., and got a lot of success in solving complex tasks. In this paper, a brief overview of the application of reinforcement algorithms in robotic science is presented. This survey offered a comprehensive review based on segments as (1) development of RL (2) types of RL algorithm like; Actor-Critic, DeepRL, multi-agent RL and Human-centered algorithm (3) various applications of RL in robotics based on their usage platforms such as land-based, water-based and air-based, (4) RL algorithms/mechanism used in robotic applications. Finally, an open discussion is provided that potentially raises a range of future research directions in robotics. The objective of this survey is to present a guidance point for future research in a more meaningful direction.
Similar content being viewed by others
References
Abul O, Polat F, Alhajj R (2000) Multiagent reinforcement learning using function approximation. IEEE Trans Syst Man Cybern Part C (Appl Rev) 30(4):485–497
Adam S, Busoniu L, Babuska R (2011) Experience replay for real-time reinforcement learning control. IEEE Trans Syst Man Cybern Part C (Appl Rev) 42(2):201–212
Ansari Y, Manti M, Falotico E, Cianchetti M, Laschi C (2017) Multiobjective optimization for stiffness and position control in a soft robot arm module. IEEE Robot Autom Lett 3(1):108–115
Antonelo EA, Schrauwen B (2014) On learning navigation behaviors for small mobile robots with reservoir computing architectures. IEEE Trans Neural Netw Learn Syst 26(4):763–780
Arulkumaran K, Deisenroth MP, Brundage M, Bharath AA (2017) Deep reinforcement learning: a brief survey. IEEE Signal Process Mag 34(6):26–38
Averbeck BB, Costa VD (2017) Motivational neural circuits underlying reinforcement learning. Nat Neurosci 20(4):505–512
Baird L (1995) Residual algorithms: reinforcement learning with function approximation. In: Machine learning proceedings 1995, Elsevier, pp 30–37
Baird III LC, Moore AW (1999) Gradient descent for general reinforcement learning. In: Advances in neural information processing systems, pp 968–974
Barto AG, Sutton RS, Anderson CW (1983) Neuron like adaptive elements that can solve difficult learning control problems. IEEE Trans Syst Man Cybern 5:834–846
Bejar E, Moran A (2019) A preview neuro-fuzzy controller based on deep reinforcement learning for backing up a truck-trailer vehicle. In: 2019 IEEE canadian conference of electrical and computer engineering (CCECE), IEEE, pp 1–4
Beom HR, Cho HS (1995) A sensor-based navigation for a mobile robot using fuzzy logic and reinforcement learning. IEEE Trans Syst Man Cybern 25(3):464–477
Bertsekas DP (1995) Dynamic programming and optimal control. Athena scientific, Belmont
Bertsekas DP (2018) Feature-based aggregation and deep reinforcement learning: a survey and some new implementations. IEEE/CAA J Autom Sin 6(1):1–31
Böhmer W, Springenberg JT, Boedecker J, Riedmiller M, Obermayer K (2015) Autonomous learning of state representations for control: an emerging field aims to autonomously learn state representations for reinforcement learning agents from their real-world sensor observations. KI-Künstliche Intelligenz 29(4):353–362
Bonarini A, Bonacina C, Matteucci M (2001) An approach to the design of reinforcement functions in real world, agent-based applications. IEEE Trans Syst Man Cybern Part B (Cybern) 31(3):288–301
Bowling M, Veloso M (2001) Rational and convergent learning in stochastic games. Int Joint Conf Artif Intell 17:1021–1026
Bowling M, Veloso M (2002) Multiagent learning using a variable learning rate. Artif Intell 136(2):215–250
Boyan JA (2002) Technical update: least-squares temporal difference learning. Mach Learn 49(2–3):233–246
Bradtke SJ, Ydstie BE, Barto AG (1994) Adaptive linear quadratic control using policy iteration. In: Proceedings of 1994 American control conference-ACC’94, IEEE, vol 3, pp 3475–3479
Breyer M, Furrer F, Novkovic T, Siegwart R, Nieto J (2019) Comparing task simplifications to learn closed-loop object picking using deep reinforcement learning. IEEE Robot Autom Lett 4(2):1549–1556
Bu L, Babu R, De Schutter B et al (2008) A comprehensive survey of multiagent reinforcement learning. IEEE Trans Syst Man Cybern Part C (Appl Rev) 38(2):156–172
Caarls W, Schuitema E (2015) Parallel online temporal difference learning for motor control. IEEE Trans Neural Netw Learn Syst 27(7):1457–1468
Cao X, Sun C, Yan M (2019) Target search control of auv in underwater environment with deep reinforcement learning. IEEE Access 7:96549–96559
Carlucho I, De Paula M, Villar SA, Acosta GG (2017) Incremental q-learning strategy for adaptive pid control of mobile robots. Expert Syst Appl 80:183–199
Carlucho I, De Paula M, Wang S, Petillot Y, Acosta GG (2018) Adaptive low-level control of autonomous underwater vehicles using deep reinforcement learning. Robot Auton Syst 107:71–86
Chalvatzaki G, Papageorgiou XS, Maragos P, Tzafestas CS (2019) Learn to adapt to human walking: a model-based reinforcement learning approach for a robotic assistant rollator. IEEE Robot Autom Lett 4(4):3774–3781
Cheng Y, Zhang W (2018) Concise deep reinforcement learning obstacle avoidance for underactuated unmanned marine vessels. Neurocomputing 272:63–73
Colomé A, Torras C (2018) Dimensionality reduction for dynamic movement primitives and application to bimanual manipulation of clothes. IEEE Trans Robot 34(3):602–615
Cruz F, Magg S, Weber C, Wermter S (2016) Training agents with interactive reinforcement learning and contextual affordances. IEEE Trans Cogn Dev Syst 8(4):271–284
Cutler M, Walsh TJ, How JP (2015) Real-world reinforcement learning via multifidelity simulators. IEEE Trans Robot 31(3):655–671
Da Silva B, Konidaris G, Barto A (2012) Learning parameterized skills. Preprint arXiv:12066398
Dai X, Li CK, Rad AB (2005) An approach to tune fuzzy controllers based on reinforcement learning for autonomous vehicle control. IEEE Trans Intell Transp Syst 6(3):285–293
Dayan P, Niv Y (2008) Reinforcement learning: the good, the bad and the ugly. Curr Opin Neurobiol 18(2):185–196
de Bruin T, Kober J, Tuyls K, Babuška R (2018) Integrating state representation learning into deep reinforcement learning. IEEE Robot Autom Lett 3(3):1394–1401
Deisenroth M, Rasmussen CE (2011) Pilco: a model-based and data-efficient approach to policy search. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 465–472
Deisenroth MP, Fox D, Rasmussen CE (2013a) Gaussian processes for data-efficient learning in robotics and control. IEEE Trans Pattern Anal Mach Intell 37(2):408–423
Deisenroth MP, Neumann G, Peters J et al (2013b) A survey on policy search for robotics. Found Trends Robot 2(1–2):1–142
Deng Z, Guan H, Huang R, Liang H, Zhang L, Zhang J (2017) Combining model-based \(q\)-learning with structural knowledge transfer for robot skill learning. IEEE Trans Cogn Dev Syst 11(1):26–35
Dong D, Chen C, Chu J, Tarn TJ (2010) Robust quantum-inspired reinforcement learning for robot navigation. IEEE/ASME Trans Mech 17(1):86–97
Doroodgar B, Liu Y, Nejat G (2014) A learning-based semi-autonomous controller for robotic exploration of unknown disaster scenes while searching for victims. IEEE Trans Cybern 44(12):2719–2732
Doshi-Velez F, Pfau D, Wood F, Roy N (2013) Bayesian nonparametric methods for partially-observable reinforcement learning. IEEE Trans Pattern Anal Mach Intell 37(2):394–407
Duan Y, Cui BX, Xu XH (2012) A multi-agent reinforcement learning approach to robot soccer. Artif Intell Rev 38(3):193–211
El-Fakdi A, Carreras M (2013) Two-step gradient-based reinforcement learning for underwater robotics behavior learning. Robot Auton Syst 61(3):271–282
Er MJ, Deng C (2005) Obstacle avoidance of a mobile robot using hybrid learning approach. IEEE Trans Ind Electron 52(3):898–905
Falco P, Attawia A, Saveriano M, Lee D (2018) On policy learning robust to irreversible events: an application to robotic in-hand manipulation. IEEE Robot Autom Lett 3(3):1482–1489
Farahmand AM, Ahmadabadi MN, Lucas C, Araabi BN (2009) Interaction of culture-based learning and cooperative co-evolution and its application to automatic behavior-based system design. IEEE Trans Evol Comput 14(1):23–57
Faust A, Ruymgaart P, Salman M, Fierro R, Tapia L (2014) Continuous action reinforcement learning for control-affine systems with unknown dynamics. IEEE/CAA J Autom Sin 1(3):323–336
Foglino F, Christakou CC, Leonetti M (2019) An optimization framework for task sequencing in curriculum learning. In: 2019 Joint IEEE 9th international conference on development and learning and epigenetic robotics (ICDL-EpiRob), IEEE, pp 207–214
Frost G, Maurelli F, Lane DM (2015) Reinforcement learning in a behaviour-based control architecture for marine archaeology. In: OCEANS 2015-Genova, IEEE, pp 1–5
Fu C, Chen K (2008) Gait synthesis and sensory control of stair climbing for a humanoid robot. IEEE Trans Ind Electron 55(5):2111–2120
Gordon GJ (1995) Stable function approximation in dynamic programming. In: Machine learning proceedings 1995, Elsevier, pp 261–268
Gosavi A (2009) Reinforcement learning: a tutorial survey and recent advances. INFORMS J Comput 21(2):178–192
Gottipati SK, Seo K, Bhatt D, Mai V, Murthy K, Paull L (2019) Deep active localization. IEEE Robot Autom Lett 4(4):4394–4401
Greenwald A, Hall K, Serrano R (2003) Correlated q-learning. ICML 3:242–249
Grigorescu S, Trasnea B, Marina L, Vasilcoi A, Cocias T (2019) Neurotrajectory: a neuroevolutionary approach to local state trajectory learning for autonomous vehicles. Preprint arXiv:190610971
Grondman I, Busoniu L, Lopes GA, Babuska R (2012) A survey of actor-critic reinforcement learning: standard and natural policy gradients. IEEE Trans Syst Man Cybern Part C (Appl Rev) 42(6):1291–1307
Gu D, Hu H (2007) Integration of coordination architecture and behavior fuzzy learning in quadruped walking robots. IEEE Trans Syst Man Cybern Part C (Appl Rev) 37(4):670–681
Gullapalli V (1990) A stochastic reinforcement learning algorithm for learning real-valued functions. Neural Netw 3(6):671–692
Guo M, Liu Y, Malec J (2004) A new q-learning algorithm based on the metropolis criterion. IEEE Trans Syst Man Cybern Part B (Cybern) 34(5):2140–2143
Hansen N, Ostermeier A (2001) Completely derandomized self-adaptation in evolution strategies. Evol Comput 9(2):159–195
Hasegawa Y, Fukuda T, Shimojima K (1999) Self-scaling reinforcement learning for fuzzy logic controller-applications to motion control of two-link brachiation robot. IEEE Trans Ind Electron 46(6):1123–1131
Hazara M, Kyrki V (2019) Transferring generalizable motor primitives from simulation to real world. IEEE Robot Autom Lett 4(2):2172–2179
He W, Li Z, Chen CP (2017) A survey of human-centered intelligent robots: issues and challenges. IEEE/CAA J Autom Sin 4(4):602–609
Heidrich-Meisner V, Igel C (2008) Evolution strategies for direct policy search. In: International conference on parallel problem solving from nature, Springer, pp 428–437
Ho MK, Littman ML, Cushman F, Austerweil JL (2015) Teaching with rewards and punishments: Reinforcement or communication? In: CogSci
Hu H, Song S, Chen CP (2019) Plume tracing via model-free reinforcement learning method. IEEE Trans Neural Netw Learn Syst
Hu J, Wellman MP (2003) Nash q-learning for general-sum stochastic games. J Mach Learn Res 4:1039–1069
Hu J, Zhang H, Song L (2018) Reinforcement learning for decentralized trajectory design in cellular uav networks with sense-and-send protocol. IEEE Internet of Things Journal
Huang R, Cheng H, Qiu J, Zhang J (2019) Learning physical human–robot interaction with coupled cooperative primitives for a lower exoskeleton. IEEE Trans Autom Scie Eng
Huang Z, Xu X, He H, Tan J, Sun Z (2017) Parameterized batch reinforcement learning for longitudinal control of autonomous land vehicles. IEEE Trans Syst Man Cybern Syst 49(4):730–741
Hung SM, Givigi SN (2016) A q-learning approach to flocking with uavs in a stochastic environment. IEEE Trans Cybern 47(1):186–197
Hwang KS, Lo CY, Liu WL (2009) A modular agent architecture for an autonomous robot. IEEE Trans Instrum Meas 58(8):2797–2806
Hwang KS, Lin JL, Yeh KH (2015) Learning to adjust and refine gait patterns for a biped robot. IEEE Trans Syst Man Cybern Syst 45(12):1481–1490
Hwangbo J, Sa I, Siegwart R, Hutter M (2017) Control of a quadrotor with reinforcement learning. IEEE Robot Autom Lett 2(4):2096–2103
Iwata K, Ikeda K, Sakai H (2004) A new criterion using information gain for action selection strategy in reinforcement learning. IEEE Trans Neural Netw 15(4):792–799
Juang CF, Hsu CH (2009) Reinforcement ant optimized fuzzy controller for mobile-robot wall-following control. IEEE Trans Ind Electron 56(10):3931–3940
Kaelbling LP, Littman ML, Moore AW (1996) Reinforcement learning: a survey. J Artif Intell Res 4:237–285
Kamio S, Iba H (2005) Adaptation technique for integrating genetic programming and reinforcement learning for real robots. IEEE Trans Evol Comput 9(3):318–333
Khamassi M, Velentzas G, Tsitsimis T, Tzafestas C (2018) Robot fast adaptation to changes in human engagement during simulated dynamic social interaction with active exploration in parameterized reinforcement learning. IEEE Trans Cogn Dev Syst 10(4):881–893
Kim B, Park J, Park S, Kang S (2009) Impedance learning for robotic contact tasks using natural actor-critic algorithm. IEEE Trans Syst Man Cybern Part B (Cybern) 40(2):433–443
Kiumarsi B, Vamvoudakis KG, Modares H, Lewis FL (2017) Optimal and autonomous control using reinforcement learning: a survey. IEEE Trans Neural Netw Learn Syst 29(6):2042–2062
Kober J, Peters J (2011) Policy search for motor primitives in robotics. Mach Learn 84:171–203
Kober J, Bagnell JA, Peters J (2013) Reinforcement learning in robotics: a survey. Int J Robot Res 32(11):1238–1274
Koç O, Peters J (2019) Learning to serve: an experimental study for a new learning from demonstrations framework. IEEE Robot Autom Lett 4(2):1784–1791
Konda VR, Tsitsiklis JN (2000) Actor-critic algorithms. In: Advances in neural information processing systems, pp 1008–1014
Koryakovskiy I, Kudruss M, Vallery H, Babuška R, Caarls W (2018) Model-plant mismatch compensation using reinforcement learning. IEEE Robot Autom Lett 3(3):2471–2477
La HM, Lim R, Sheng W (2014) Multirobot cooperative learning for predator avoidance. IEEE Trans Control Syst Technol 23(1):52–63
Lambert NO, Drew DS, Yaconelli J, Levine S, Calandra R, Pister KS (2019) Low-level control of a quadrotor with deep model-based reinforcement learning. IEEE Robot Autom Lett 4(4):4224–4230
Lasheng Y, Zhongbin J, Kang L (2012) Research on task decomposition and state abstraction in reinforcement learning. Artif Intell Rev 38(2):119–127
Le TP, Ngo VA, Jaramillo PM, Chung T (2019) Importance sampling policy gradient algorithms in reproducing kernel hilbert space. Artif Intell Rev 52(3):2039–2059
Li G, Gomez R, Nakamura K, He B (2019) Human-centered reinforcement learning: a survey. IEEE Trans Hum Mach Syst
Li THS, Su YT, Lai SW, Hu JJ (2010) Walking motion generation, synthesis, and control for biped robot by using pgrl, lpi, and fuzzy logic. IEEE Trans Syst Man Cybern Part B (Cybern) 41(3):736–748
Li Z, Liu J, Huang Z, Peng Y, Pu H, Ding L (2017a) Adaptive impedance control of human-robot cooperation using reinforcement learning. IEEE Trans Ind Electron 64(10):8013–8022
Li Z, Zhao T, Chen F, Hu Y, Su CY, Fukuda T (2017b) Reinforcement learning of manipulation and grasping using dynamical movement primitives for a humanoidlike mobile manipulator. IEEE/ASME Trans Mech 23(1):121–131
Lin JL, Hwang KS, Wang YL (2013) A simple scheme for formation control based on weighted behavior learning. IEEE Trans Neural Netw Learn Syst 25(6):1033–1044
Lin Y, Makedon F, Xu Y (2011) Episodic task learning in markov decision processes. Artif Intell Rev 36(2):87–98
Littman ML (2015) Reinforcement learning improves behaviour from evaluative feedback. Nature 521(7553):445–451
Liu S, Ngiam KY, Feng M (2019) Deep reinforcement learning for clinical decision support: a brief survey. Preprint arXiv:190709475
Luo B, Liu D, Huang T, Liu J (2017) Output tracking control based on adaptive dynamic programming with multistep policy evaluation. IEEE Trans Syst Man Cybern Syst
Luo B, Yang Y, Liu D (2018) Adaptive q-learning for data-based optimal output regulation with experience replay. IEEE Trans Cybern 48(12):3337–3348
Luo B, Yang Y, Liu D, Wu HN (2019) Event-triggered optimal control with performance guarantees using adaptive dynamic programming. IEEE Trans Neural Netw Learn Syst 31(1):76–88
Lv L, Zhang S, Ding D, Wang Y (2019) Path planning via an improved dqn-based learning policy. IEEE Access
Madden MG, Howley T (2004) Transfer of experience between reinforcement learning environments with progressive difficulty. Artif Intell Rev 21(3–4):375–398
Markova VD, Shopov VK (2019) Knowledge transfer in reinforcement learning agent. In: 2019 international conference on information technologies (InfoTech), IEEE, pp 1–4
McPartland M, Gallagher M (2010) Reinforcement learning in first person shooter games. IEEE Trans Comput Intell AI Games 3(1):43–56
Meeden LA (1996) An incremental approach to developing intelligent neural network controllers for robots. IEEE Trans Syst Man Cybern Part B (Cybern) 26(3):474–485
Melo FS, Meyn SP, Ribeiro MI (2008) An analysis of reinforcement learning with function approximation. In: Proceedings of the 25th international conference on Machine learning, ACM, pp 664–671
Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G et al (2015a) Human-level control through deep reinforcement learning. Nature 518(7540):529
Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G et al (2015b) Human-level control through deep reinforcement learning. Nature 518(7540):529–533
Modares H, Ranatunga I, Lewis FL, Popa DO (2015) Optimized assistive human–robot interaction using reinforcement learning. IEEE Trans Cybern 46(3):655–667
Modares H, Lewis FL, Kang W, Davoudi A (2017) Optimal synchronization of heterogeneous nonlinear systems with unknown dynamics. IEEE Trans Autom Control 63(1):117–131
Muelling K, Kober J, Peters J (2010) Learning table tennis with a mixture of motor primitives. In: 2010 10th IEEE-RAS international conference on humanoid robots, IEEE, pp 411–416
Neftci E, Averbeck B (2019) Reinforcement learning in artificial and biological systems. Nat Mach Intell. https://doi.org/10.1038/s42256-019-0025-4
Neftci EO, Averbeck BB (2002) Reinforcement learning in artificial and biological systems. Environment p 3
Ng AY, Harada D, Russell S (1999) Policy invariance under reward transformations: theory and application to reward shaping. ICML 99:278–287
Nguyen ND, Nguyen T, Nahavandi S (2017) System design perspective for human-level agents using deep reinforcement le arning: a survey. IEEE Access 5:27091–27102
Nguyen TT, Nguyen ND, Nahavandi S (2018) Deep reinforcement learning for multi-agent systems: a review of challenges, solutions and applications. Preprint arXiv:181211794
O’Flaherty R, Egerstedt M (2014) Low-dimensional learning for complex robots. IEEE Trans Autom Sci Eng 12(1):19–27
Ohnishi M, Wang L, Notomista G, Egerstedt M (2019) Barrier-certified adaptive reinforcement learning with applications to brushbot navigation. IEEE Trans Robot 35(5):1186–1205
Palomeras N, El-Fakdi A, Carreras M, Ridao P (2012) Cola2: a control architecture for auvs. IEEE J Ocean Eng 37(4):695–716
Parunak HVD (1999) Industrial and practical applications of dai. Multiagent systems: a modern approach to distributed artificial intelligence pp 337–421
Peters J, Schaal S (2008) Reinforcement learning of motor skills with policy gradients. Neural netw 21(4):682–697
Peters J, Vijayakumar S, Schaal S (2005) Natural actor-critic. In: European conference on machine learning, Springer, pp 280–291
Peters J, Mulling K, Altun Y (2010) Relative entropy policy search. In: Twenty-Fourth AAAI conference on artificial intelligence
Plaza MG, Martínez-Marín T, Prieto SS, Luna DM (2009) Integration of cell-mapping and reinforcement-learning techniques for motion planning of car-like robots. IEEE Trans Instrum Meas 58(9):3094–3103
Polat F et al (2002) Learning intelligent behavior in a non-stationary and partially observable environment. Artif Intell Rev 18(2):97–115
Puterman ML (2014) Markov decision processes: discrete stochastic dynamic programming. John Wiley & Sons
Rescorla R, Wagner A, Black AH, Prokasy WF (1972) Classical conditioning ii: current research and theory pp 64–99
Ribeiro C (2002) Reinforcement learning agents. Artif Intell Rev 17(3):223–250
Riedmiller M, Peters J, Schaal S (2007) Evaluation of policy gradient methods and variants on the cart-pole benchmark. In: 2007 IEEE international symposium on approximate dynamic programming and reinforcement learning, IEEE, pp 254–261
Rombokas E, Malhotra M, Theodorou EA, Todorov E, Matsuoka Y (2012) Reinforcement learning and synergistic control of the act hand. IEEE/ASME Trans Mech 18(2):569–577
Roveda L, Pallucca G, Pedrocchi N, Braghin F, Tosatti LM (2017) Iterative learning procedure with reinforcement for high-accuracy force tracking in robotized tasks. IEEE Trans Ind Inform 14(4):1753–1763
Rummery GA, Niranjan M (1994) On-line Q-learning using connectionist systems, vol 37. University of Cambridge, Department of Engineering Cambridge, England
Rylatt M, Czarnecki C, Routen T (1998) Connectionist learning in behaviour-based mobile robots: a survey. Artif Intell Rev 12(6):445–468
Sallab AE, Abdou M, Perot E, Yogamani S (2017) Deep reinforcement learning framework for autonomous driving. Electron Imaging 19:70–76
dos Santos SRB, Givigi SN, Nascimento CL (2015) Autonomous construction of multiple structures using learning automata: description and experimental validation. IEEE Syst J 9(4):1376–1387
Santucci VG, Baldassarre G, Cartoni E (2019) Autonomous reinforcement learning of multiple interrelated tasks. Preprint arXiv:190601374
Schaul T, Horgan D, Gregor K, Silver D (2015) Universal value function approximators. In: International conference on machine learning, pp 1312–1320
Sharma R, Gopal M (2008) A markov game-adaptive fuzzy controller for robot manipulators. IEEE Trans Fuzzy Syst 16(1):171–186
Sharma RS, Nair RR, Agrawal P, Behera L, Subramanian VK (2018) Robust hybrid visual servoing using reinforcement learning and finite-time adaptive fosmc. IEEE Syst J
Shi H, Li X, Hwang KS, Pan W, Xu G (2016) Decoupled visual servoing with fuzzyq-learning. IEEE Trans Ind Inform 14(1):241–252
Silver D, Lever G, Heess N, Degris T, Wierstra D, Riedmiller M (2014) Deterministic policy gradient algorithms
Stanley KO, Clune J, Lehman J, Miikkulainen R (2019) Designing neural networks through neuroevolution. Nat Mach Intell 1(1):24–35
Stone P, Veloso M (2000) Multiagent systems: a survey from a machine learning perspective. Auton Robots 8(3):345–383
Stulp F, Buchli J, Ellmer A, Mistry M, Theodorou EA, Schaal S (2012) Model-free reinforcement learning of impedance control in stochastic environments. IEEE Trans Auton Mental Dev 4(4):330–341
Such FP, Madhavan V, Conti E, Lehman J, Stanley KO, Clune J (2017) Deep neuroevolution: genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning. Preprint arXiv:171206567
Sutton RS (1988) Learning to predict by the methods of temporal differences. Mach Learn 3(1):9–44
Sutton RS (1992) A special issue of machine learning on reinforcement learning. Mach Learn 8
Sutton RS, Barto AG (2018) Reinforcement learning: an introduction. MIT press
Sutton RS, McAllester DA, Singh SP, Mansour Y (2000) Policy gradient methods for reinforcement learning with function approximation. In: Advances in neural information processing systems, pp 1057–1063
Tenorio-Gonzalez AC, Morales EF, Villaseñor-Pineda L (2010) Dynamic reward shaping: training a robot by voice. In: Ibero-American conference on artificial intelligence, Springer, pp 483–492
Theodorou E, Buchli J, Schaal S (2010) A generalized path integral control approach to reinforcement learning. J Mach Learn Res 11:3137–3181
Thomaz AL, Breazeal C (2008) Teachable robots: understanding human teaching behavior to build more effective robot learners. Artif Intell 172(6–7):716–737
Truong XT, Ngo TD (2017) Toward socially aware robot navigation in dynamic and crowded environments: a proactive social motion model. IEEE Trans Autom Sci Eng 14(4):1743–1760
Tsitsiklis JN, Van Roy B (1996) Feature-based methods for large scale dynamic programming. Mach Learn 22(1–3):59–94
Tsitsiklis JN, Van Roy B (1997) Analysis of temporal-diffference learning with function approximation. In: Advances in neural information processing systems, pp 1075–1081
Turan M, Almalioglu Y, Gilbert HB, Mahmood F, Durr NJ, Araujo H, Sarı AE, Ajay A, Sitti M (2019) Learning to navigate endoscopic capsule robots. IEEE Robot Autom Lett 4(3):3075–3082
Tzafestas SG, Rigatos GG (2002) Fuzzy reinforcement learning control for compliance tasks of robotic manipulators. IEEE Trans Syst Man Cybern Part B (Cybern) 32(1):107–113
Van Hasselt H, Guez A, Silver D (2016) Deep reinforcement learning with double q-learning. In: Thirtieth AAAI conference on artificial intelligence, IEEE, pp 2094–2100
Viseras A, Garcia R (2019) Deepig: multi-robot information gathering with deep reinforcement learning. IEEE Robot Autom Lett 4(3):3059–3066
Vlassis N (2007) A concise introduction to multiagent systems and distributed artificial intelligence. Synth Lect Artif Intell Mach Learn 1(1):1–71
Wang C, Wang J, Shen Y, Zhang X (2019) Autonomous navigation of uavs in large-scale complex environments: a deep reinforcement learning approach. IEEE Trans Veh Technol 68(3):2124–2136
Wang J, Xu X, Liu D, Sun Z, Chen Q (2013) Self-learning cruise control using kernel-based least squares policy iteration. IEEE Trans Control Syst Technol 22(3):1078–1087
Wang JP, Shi YK, Zhang WS, Thomas I, Duan SH (2018a) Multitask policy adversarial learning for human-level control with large state spaces. IEEE Trans Ind Inform 15(4):2395–2404
Wang S, Chaovalitwongse W, Babuska R (2012) Machine learning algorithms in bipedal robot control. IEEE Trans Syst Man Cybern Part C (Appl Rev) 42(5):728–743
Wang Y, Lang H, De Silva CW (2010) A hybrid visual servo controller for robust grasping by wheeled mobile robots. IEEE/ASME Trans Mech 15(5):757–769
Wang Y, He H, Sun C (2018b) Learning to navigate through complex dynamic environment with modular deep reinforcement learning. IEEE Trans Games 10(4):400–412
Watkins CJ, Dayan P (1992) Q-learning. Mach Learn 8(3–4):279–292
Watkins CJCH (1989) Learning from delayed rewards
Whitbrook AM, Aickelin U, Garibaldi JM (2007) Idiotypic immune networks in mobile-robot control. IEEE Trans Syst Man Cybern Part B (Cybern) 37(6):1581–1598
Williams RJ (1992) Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach Learn 8(3–4):229–256
Witten IH (1977) An adaptive optimal controller for discrete-time markov environments. Inf Control 34(4):286–295
Wu C, Ju B, Wu Y, Lin X, Xiong N, Xu G, Li H, Liang X (2019) Uav autonomous target search based on deep reinforcement learning in complex disaster scene. IEEE Access 7:117227–117245
Xi A, Mudiyanselage TW, Tao D, Chen C (2019) Balance control of a biped robot on a rotating platform based on efficient reinforcement learning. IEEE/CAA J Autom Sin 6(4):938–951
Xiao L, Xie C, Min M, Zhuang W (2017) User-centric view of unmanned aerial vehicle transmission against smart attacks. IEEE Trans Veh Technol 67(4):3420–3430
Xu X, Liu C, Yang SX, Hu D (2011) Hierarchical approximate policy iteration with binary-tree state space decomposition. IEEE Trans Neural Netw 22(12):1863–1877
Yang E, Gu D (2004) Multiagent reinforcement learning for multi-robot systems: a survey. Tech. rep., tech. rep
Yang X, He H, Liu D (2017) Event-triggered optimal neuro-controller design with reinforcement learning for unknown nonlinear systems. IEEE Trans Syst Man Cybern Syst
Yang Z, Merrick K, Jin L, Abbass HA (2018) Hierarchical deep reinforcement learning for continuous action control. IEEE Trans Neural Netw Learn Syst 29(11):5174–5184
Ye C, Yung NH, Wang D (2003) A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance. IEEE Trans Syst Man Cybern Part B (Cybern) 33(1):17–27
Yin S, Zhao S, Zhao Y, Yu FR (2019) Intelligent trajectory design in uav-aided communications with reinforcement learning. IEEE Trans Veh Technol 68(8):8227–8231
Yu C, Zhang M, Ren F, Tan G (2015a) Multiagent learning of coordination in loosely coupled multiagent systems. IEEE Trans Cybern 45(12):2853–2867
Yu J, Wang C, Xie G (2015b) Coordination of multiple robotic fish with applications to underwater robot competition. IEEE Trans Ind Electron 63(2):1280–1288
Yung NH, Ye C (1999) An intelligent mobile vehicle navigator based on fuzzy logic and reinforcement learning. IEEE Trans Syst Man Cybern Part B (Cybern) 29(2):314–321
Zalama E, Gomez J, Paul M, Peran JR (2002) Adaptive behavior navigation of a mobile robot. IEEE Trans Syst Man Cybern Part A Syst Hum 32(1):160–169
Zeng Y, Zhang R, Lim TJ (2016) Wireless communications with unmanned aerial vehicles: opportunities and challenges. IEEE Commun Mag 54(5):36–42
Zhang H, Jiang H, Luo Y, Xiao G (2016) Data-driven optimal consensus control for discrete-time multi-agent systems with unknown dynamics using reinforcement learning method. IEEE Trans Ind Electron 64(5):4091–4100
Zhang J, Tai L, Yun P, Xiong Y, Liu M, Boedecker J, Burgard W (2019) Vr-goggles for robots: real-to-sim domain adaptation for visual control. IEEE Robot Autom Lett 4(2):1148–1155
Zhou L, Yang P, Chen C, Gao Y (2016) Multiagent reinforcement learning with sparse interactions by negotiation and knowledge transfer. IEEE Trans Cybern 47(5):1238–1250
Zhu J, Zhu J, Wang Z, Guo S, Xu C (2018) Hierarchical decision and control for continuous multitarget problem: policy evaluation with action delay. IEEE Trans Neural Netw Learn Syst 30(2):464–473
Zhu Y, Mottaghi R, Kolve E, Lim JJ, Gupta A, Fei-Fei L, Farhadi A (2017) Target-driven visual navigation in indoor scenes using deep reinforcement learning. In: 2017 IEEE international conference on robotics and automation (ICRA), IEEE, pp 3357–3364
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Singh, B., Kumar, R. & Singh, V.P. Reinforcement learning in robotic applications: a comprehensive survey. Artif Intell Rev 55, 945–990 (2022). https://doi.org/10.1007/s10462-021-09997-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10462-021-09997-9