There is and has been a fruitful flow of concepts and ideas between studies of learning in biological and artificial systems. Much early work that led to the development of reinforcement learning (RL) algorithms for artificial systems was inspired by learning rules first developed in biology by Bush and Mosteller, and Rescorla and Wagner. More recently, temporal-difference RL, developed for learning in artificial agents, has provided a foundational framework for interpreting the activity of dopamine neurons. In this Review, we describe state-of-the-art work on RL in biological and artificial agents. We focus on points of contact between these disciplines and identify areas where future research can benefit from information flow between these fields. Most work in biological systems has focused on simple learning problems, often embedded in dynamic environments where flexibility and ongoing learning are important, similar to real-world learning problems faced by biological systems. In contrast, most work in artificial agents has focused on learning a single complex problem in a static environment. Moving forward, work in each field will benefit from a flow of ideas that represent the strengths within each discipline.
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction (MIT Press, Cambridge, 1998).
Pribram, K. H. A review of theory in physiological psychology. Annu. Rev. Psychol. 11, 1–40 (1960).
Janak, P. H. & Tye, K. M. From circuits to behaviour in the amygdala. Nature 517, 284–292 (2015).
Namburi, P. et al. A circuit mechanism for differentiating positive and negative associations. Nature 520, 675–678 (2015).
Paton, J. J., Belova, M. A., Morrison, S. E. & Salzman, C. D. The primate amygdala represents the positive and negative value of visual stimuli during learning. Nature 439, 865–870 (2006).
Hamid, A. A. et al. Mesolimbic dopamine signals the value of work. Nat. Neurosci. 19, 117–126 (2016).
Costa, V. D., Dal Monte, O., Lucas, D. R., Murray, E. A. & Averbeck, B. B. Amygdala and ventral striatum make distinct contributions to reinforcement learning. Neuron 92, 505–517 (2016).
Puterman, M. L. Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley, New York, 1994).
Bertsekas, D. P. Dynamic Programming and Optimal Control (Athena Scientific, Belmont, 1995).
Vapnik, V. The Nature of Statistical Learning Theory (Springer, New York, 2013).
Hessel, M. et al. Multi-task deep reinforcement learning with PopArt. Preprint at https://arxiv.org/abs/1809.04474 (2018).
Kirkpatrick, J. et al. Overcoming catastrophic forgetting in neural networks. Proc. Natl Acad. Sci. USA 114, 3521–3526 (2017).
Banino, A. et al. Vector-based navigation using grid-like representations in artificial agents. Nature 557, 429–433 (2018).
Mattar, M. G. & Daw, N. D. Prioritized memory access explains planning and hippocampal replay. Nat. Neurosci. 21, 1609–1617 (2018).
Rosenblatt, F. The perceptron: a probabilistic model for information-storage and organization in the brain. Psychol. Rev. 65, 386–408 (1958).
Hinton, G. E., Dayan, P., Frey, B. J. & Neal, R. M. The “wake-sleep” algorithm for unsupervised neural networks. Science 268, 1158–1161 (1995).
Rescorla, R. A. & Wagner, A. R. in Classical Conditioning II: Current Research and Theory (eds Black, A. H. & Prokasy, W. F.) 64–99 (Appleton-Century-Crofts, New York, 1972).
Schultz, W., Dayan, P. & Montague, P. R. A neural substrate of prediction and reward. Science 275, 1593–1599 (1997).
Montague, P. R., Dayan, P. & Sejnowski, T. J. A framework for mesencephalic dopamine systems based on predictive Hebbian learning. J. Neurosci. 16, 1936–1947 (1996).
Houk, J. C., Adamas, J. L. & Barto, A. G. in Models of Information Processing in the Basal Ganglia (eds Houk, J. C., Davis, J. L. & Beiser, D. G.) 249–274 (MIT Press, Cambridge, 1995).
Frank, M. J. Dynamic dopamine modulation in the basal ganglia: a neurocomputational account of cognitive deficits in medicated and nonmedicated Parkinsonism. J. Cogn. Neurosci. 17, 51–72 (2005).
Haber, S. N., Kim, K. S., Mailly, P. & Calzavara, R. Reward-related cortical inputs define a large striatal region in primates that interface with associative cortical connections, providing a substrate for incentive-based learning. J. Neurosci. 26, 8368–8376 (2006).
Mink, J. W. The basal ganglia: focused selection and inhibition of competing motor programs. Prog. Neurobiol. 50, 381–425 (1996).
Lau, B. & Glimcher, P. W. Value representations in the primate striatum during matching behavior. Neuron 58, 451–463 (2008).
O’Doherty, J. et al. Dissociable roles of ventral and dorsal striatum in instrumental conditioning. Science 304, 452–454 (2004).
Averbeck, B. B. & Costa, V. D. Motivational neural circuits underlying reinforcement learning. Nat. Neurosci. 20, 505–512 (2017).
Sutton, R. S. Learning to predict by the methods of temporal differences. Mach. Learn. 3, 9–44 (1988).
Schultz, W. Dopamine reward prediction error coding. Dialog. Clin. Neurosci. 18, 23–32 (2016).
Steinberg, E. E. et al. A causal link between prediction errors, dopamine neurons and learning. Nat. Neurosci. 16, 966–973 (2013).
Saunders, B. T., Richard, J. M., Margolis, E. B. & Janak, P. H. Dopamine neurons create Pavlovian conditioned stimuli with circuit-defined motivational properties. Nat. Neurosci. 21, 1072–1083 (2018).
Sharpe, M. J. et al. Dopamine transients are sufficient and necessary for acquisition of model-based associations. Nat. Neurosci. 20, 735–742 (2017).
Averbeck, B. B., Sohn, J. W. & Lee, D. Activity in prefrontal cortex during dynamic selection of action sequences. Nat. Neurosci. 9, 276–282 (2006).
Seo, M., Lee, E. & Averbeck, B. B. Action selection and action value in frontal-striatal circuits. Neuron 74, 947–960 (2012).
Lee, E., Seo, M., Dal Monte, O. & Averbeck, B. B. Injection of a dopamine type 2 receptor antagonist into the dorsal striatum disrupts choices driven by previous outcomes, but not perceptual inference. J. Neurosci. 35, 6298–6306 (2015).
Averbeck, B. B., Lehman, J., Jacobson, M. & Haber, S. N. Estimates of projection overlap and zones of convergence within frontal-striatal circuits. J. Neurosci. 34, 9497–9505 (2014).
Rothenhoefer, K. M. et al. Effects of ventral striatum lesions on stimulus versus action based reinforcement learning. J. Neurosci. 37, 6902–6914 (2017).
Friedman, D. P., Aggleton, J. P. & Saunders, R. C. Comparison of hippocampal, amygdala, and perirhinal projections to the nucleus accumbens: combined anterograde and retrograde tracing study in the Macaque brain. J. Comp. Neurol. 450, 345–365 (2002).
Alexander, G. E., DeLong, M. R. & Strick, P. L. Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu. Rev. Neurosci. 9, 357–381 (1986).
Averbeck, B. B. Amygdala and ventral striatum population codes implement multiple learning rates for reinforcement learning. In IEEE Symposium Series on Computational Intelligence (IEEE, 2017).
Jacobs, R. A., Jordan, M. I., Nowlan, S. J. & Hinton, G. E. Adaptive mixtures of local experts. Neural Comput. 3, 79–87 (1991).
Pfister, J. P., Toyoizumi, T., Barber, D. & Gerstner, W. Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning. Neural Comput. 18, 1318–1348 (2006).
Benna, M. K. & Fusi, S. Computational principles of biological memory. Preprint at https://arxiv.org/abs/1507.07580 (2015).
Lahiri, S. & Ganguli, S. A memory frontier for complex synapses. In Advances in Neural Information Processing Systems Vol. 26 (eds Burges, C. J. C., Bottou, L., Welling, M., Ghahramani, Z. & Weinberger, K. Q.) 1034–1042 (NIPS, 2013).
Koutnik, J., Greff, K., Gomez, F. & Schmidhuber, J. A clockwork RNN. Preprint at https://arxiv.org/abs/1402.3511 (2014).
Neil, D., M., P. & Liu, S.-C. Phased LSTM: accelerating recurrent network training for long or event-based sequences. In Advances in Neural Information Processing Systems Vol. 29 (eds Lee, D. D., Sugiyama, M., Luxburg, U. V., Guyon, I. & Garnett, R.) 3882–3890 (NIPS, 2016).
Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).
O’Reilly, R. C. & Frank, M. J. Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia. Neural Comput. 18, 283–328 (2006).
Bishop, C. M. Pattern Recognition and Machine Learning (Springer, New York, 2006).
Bottou, L. & LeCun, Y. Large scale online learning. In Advances in Neural Information Processing Systems Vol. 16 (eds Thrun, S., Saul, L. K. & Schölkopf, B.) (NIPS, 2004).
McCloskey, M. & Cohen, N. J. in Psychology of Learning and Motivation : Advances in Research and Theory Vol. 24 (ed. Bower, G. H.) 109–165 (1989).
McClelland, J. L., McNaughton, B. L. & O’Reilly, R. C. Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychol. Rev. 102, 419–457 (1995).
Kumaran, D., Hassabis, D. & McClelland, J. L. What learning systems do intelligent agents need? Complementary learning systems theory updated. Trends Cogn. Sci. 20, 512–534 (2016).
Mnih, V. et al. Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015).
Lin, L.-J. Self-improving reactive agents based on reinforcement learning, planning and teaching. Mach. Learn. 8, 293–321 (1992).
Zenke, F., Poole, B. & Ganguli, S. Continual learning through synaptic intelligence. Preprint at https://arxiv.org/abs/1703.04200 (2017).
Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M. & Tuytelaars, T. Memory aware synapses: learning what (not) to forget. Preprint at https://arxiv.org/abs/1711.09601 (2017).
Daw, N. D., Niv, Y. & Dayan, P. Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nat. Neurosci. 8, 1704–1711 (2005).
Costa, V. D., Tran, V. L., Turchi, J. & Averbeck, B. B. Reversal learning and dopamine: a Bayesian perspective. J. Neurosci. 35, 2407–2416 (2015).
Daw, N. D., Gershman, S. J., Seymour, B., Dayan, P. & Dolan, R. J. Model-based influences on humans’ choices and striatal prediction errors. Neuron 69, 1204–1215 (2011).
Glascher, J., Daw, N., Dayan, P. & O’Doherty, J. P. States versus rewards: dissociable neural prediction error signals underlying model-based and model-free reinforcement learning. Neuron 66, 585–595 (2010).
Doll, B. B., Simon, D. A. & Daw, N. D. The ubiquity of model-based reinforcement learning. Curr. Opin. Neurobiol. 22, 1075–1081 (2012).
Wunderlich, K., Smittenaar, P. & Dolan, R. J. Dopamine enhances model-based over model-free choice behavior. Neuron 75, 418–424 (2012).
Miller, E. K. The prefrontal cortex and cognitive control. Nat. Rev. Neurosci. 1, 59–65 (2000).
Balleine, B. W. & Dickinson, A. Goal-directed instrumental action: contingency and incentive learning and their cortical substrates. Neuropharmacology 37, 407–419 (1998).
Deserno, L. et al. Ventral striatal dopamine reflects behavioral and neural signatures of model-based control during sequential decision making. Proc. Natl Acad. Sci. USA 112, 1595–1600 (2015).
Harlow, H. F. The formation of learning sets. Psychol. Rev. 56, 51–65 (1949).
Iversen, S. D. & Mishkin, M. Perseverative interference in monkeys following selective lesions of the inferior prefrontal convexity. Exp. Brain Res. 11, 376–386 (1970).
Jang, A. I. et al. The role of frontal cortical and medial-temporal lobe brain areas in learning a Bayesian prior belief on reversals. J. Neurosci. 35, 11751–11760 (2015).
Wang, J. X. et al. Prefrontal cortex as a meta-reinforcement learning system. Nat. Neurosci. 21, 860–868 (2018).
Wilson, R. C., Takahashi, Y. K., Schoenbaum, G. & Niv, Y. Orbitofrontal cortex as a cognitive map of task space. Neuron 81, 267–279 (2014).
Schuck, N. W., Cai, M. B., Wilson, R. C. & Niv, Y. Human orbitofrontal cortex represents a cognitive map of state space. Neuron 91, 1402–1412 (2016).
DeGroot, M. H. Optimal Statistical Decisions (Wiley, Hoboken, 1970).
Starkweather, C. K., Babayan, B. M., Uchida, N. & Gershman, S. J. Dopamine reward prediction errors reflect hidden-state inference across time. Nat. Neurosci. 20, 581–589 (2017).
Starkweather, C. K., Gershman, S. J. & Uchida, N. The medial prefrontal cortex shapes dopamine reward prediction errors under state uncertainty. Neuron 98, 616–629 (2018).
Wang, X. J. Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory. J. Neurosci. 19, 9587–9603 (1999).
Schöner, G. in The Cambridge Handbook of Computational Psychology (ed. Sun, R.) 101–126 (Cambridge Univ. Press, Cambridge, 2008).
Averbeck, B. B. Theory of choice in bandit, information sampling and foraging tasks. PLoS Comput. Biol. 11, e1004164 (2015).
Otto, A. R., Raio, C. M., Chiang, A., Phelps, E. A. & Daw, N. D. Working-memory capacity protects model-based learning from stress. Proc. Natl Acad. Sci. USA 110, 20941–20946 (2013).
Akam, T., Costa, R. & Dayan, P. Simple plans or sophisticated habits? State, transition and learning interactions in the two-step task. PLoS. Comput. Biol. 11, e1004648 (2015).
Riesenhuber, M. & Poggio, T. Hierarchical models of object recognition in cortex. Nat. Neurosci. 2, 1019–1025 (1999).
Williams, R. J. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn. 8, 229–256 (1992).
Tesauro, G. Temporal difference learning and TD-Gammon. Commun. ACM 38, 58–68 (1995).
Pomerleau, D. A. ALVINN: An autonomous land vehicle in a neural network. In Advances in Neural Information Processing Systems Vol. 1 (ed. Touretzky, D. S.) (NIPS, 1988).
Levine, S., Finn, C., Darrell, T. & Abbeel, P. End-to-end training of deep visuomotor policies. J. Mach. Learn. Res. 17, 1334–1373 (2016).
Gu, S., Lillicrap, T., Sutskever, I. & Levine, S. Continuous deep Q-learning with model-based acceleration. In Proc. 33rd International Conference on Machine Learning Vol. 48 2829–2838 (PMLR, 2016).
Burda, Y., Edwards, H., Storkey, A. & Klimov, O. Exploration by random network distillation. Preprint at https://arxiv.org/abs/1810.12894 (2018).
Vezhnevets, A. S. et al. Feudal networks for hierarchical reinforcement learning. Preprint at https://arxiv.org/abs/1703.01161 (2017).
Neftci, E. O. Data and power efficient intelligence with neuromorphic learning machines. iScience 5, 52–68 (2018).
Kaiser, J., Mostafa, H. & Neftci, E. O. Synaptic plasticity dynamics for deep continuous local learning. Preprint at https://arxiv.org/abs/1811.10766 (2018).
Neftci, E. O., Augustine, C., Paul, S. & Detorakis, G. Event-driven random back-propagation: enabling neuromorphic deep learning machines. Front. Neurosci. 11, 324 (2017).
Lillicrap, T. P., Cownden, D., Tweed, D. B. & Akerman, C. J. Random synaptic feedback weights support error backpropagation for deep learning. Nat. Commun. 7, 13276 (2016).
Levine, S., Pastor, P., Krizhevsky, A., Ibarz, J. & Quillen, D. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. Int. J. Robot. Res. 37, 421–436 (2018).
Blundell, C. et al. Model-free episodic control. Preprint at https://arxiv.org/abs/1606.04460 (2016).
Gershman, S. J. & Daw, N. D. Reinforcement learning and episodic memory in humans and animals: an integrative framework. Ann. Rev. Psychol. 68, 101–128 (2017).
Finn, C., Abbeel, P. & Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks. Preprint at https://arxiv.org/abs/1703.03400 (2017).
Ha, D. & Schmidhuber, J. World models. Preprint at https://arxiv.org/abs/1803.10122 (2018).
Zambaldi, V. et al. Relational deep reinforcement learning. Preprint at https://arxiv.org/abs/1806.01830 (2018).
Daw, N. D., O’Doherty, J. P., Dayan, P., Seymour, B. & Dolan, R. J. Cortical substrates for exploratory decisions in humans. Nature 441, 876–879 (2006).
Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annu. Rev. Vision Sci. 1, 417–446 (2015).
Bernacchia, A., Seo, H., Lee, D. & Wang, X. J. A reservoir of time constants for memory traces in cortical neurons. Nat. Neurosci. 14, 366–372 (2011).
Walton, M. E., Behrens, T. E., Buckley, M. J., Rudebeck, P. H. & Rushworth, M. F. Separable learning systems in the macaque brain and the role of orbitofrontal cortex in contingent learning. Neuron 65, 927–939 (2010).
Iglesias, S. et al. Hierarchical prediction errors in midbrain and basal forebrain during sensory learning. Neuron 80, 519–530 (2013).
Badre, D. & Frank, M. J. Mechanisms of hierarchical reinforcement learning in cortico-striatal circuits 2: evidence from fMRI. Cereb. Cortex 22, 527–536 (2012).
Frank, M. J. & Badre, D. Mechanisms of hierarchical reinforcement learning in corticostriatal circuits 1: computational analysis. Cereb. Cortex 22, 509–526 (2012).
Botvinick, M. M. Hierarchical models of behavior and prefrontal function. Trends Cogn. Sci. 12, 201–208 (2008).
Botvinick, M. M., Niv, Y. & Barto, A. C. Hierarchically organized behavior and its neural foundations: a reinforcement learning perspective. Cognition 113, 262–280 (2009).
Ribas-Fernandes, J. J. et al. A neural signature of hierarchical reinforcement learning. Neuron 71, 370–379 (2011).
Botvinick, M. M. Hierarchical reinforcement learning and decision making. Curr. Opin. Neurobiol. 22, 956–962 (2012).
Botvinick, M. & Weinstein, A. Model-based hierarchical reinforcement learning and human action control. Philos. Trans. R. Soc. Lond. B 369, 20130480 (2014).
Dayan, P. & Hinton, G. E. Feudal reinforcement learning. In Advances in Neural Information Processing Systems Vol. 5 (eds Hanson, S. J., Cowan, J. D. & Giles, C. L.) 271–278 (NIPS, 1992).
Sutton, R. S., Precup, D. & Singh, S. Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning. Artif. Intell. 112, 181–211 (1999).
Bacon, P. L., Harb, J. & Precup, D. The option-critic architecture. Proc. Thirty-First AAAI Conference on Artificial Intelligence 1726–1734 (AAAI, 2017).
Jaderberg, M. et al. Reinforcement learning with unsupervised auxiliary tasks. Preprint at https://arxiv.org/abs/1611.05397 (2016).
Friston, K. The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127–138 (2010).
Ross, S., Gordon, G. J. & Bagnell, J. A. A reduction of imitation learning and structured prediction to no-regret online learning. Preprint at https://arxiv.org/abs/1011.0686 (2010).
Le, H. M. et al. Hierarchical imitation and reinforcement learning. Preprint at https://arxiv.org/abs/1803.00590 (2018).
Koechlin, E., Ody, C. & Kouneiher, F. The architecture of cognitive control in the human prefrontal cortex. Science 302, 1181–1185 (2003).
Badre, D. & D’Esposito, M. Functional magnetic resonance imaging evidence for a hierarchical organization of the prefrontal cortex. J. Cogn. Neurosci. 19, 2082–2099 (2007).
Muessgens, D., Thirugnanasambandam, N., Shitara, H., Popa, T. & Hallett, M. Dissociable roles of preSMA in motor sequence chunking and hand switching—a TMS study. J. Neurophysiol. 116, 2637–2646 (2016).
Sabour, S., Frosst, N. & Hinton, G. E. Dynamic routing between capsules. In Advances in Neural Information Processing Systems Vol. 30 (eds Guyon, I. et al.) 3856–3866 (2017).
Davies, M. et al. Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro 38, 82–99 (2018).
Friedmann, S. & Schemmel, J. Demonstrating hybrid learning in a flexible neuromorphic hardware system. Preprint at https://arxiv.org/abs/1604.05080 (2016).
Qiao, N. et al. A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses. Front. Neurosci. 9, 141 (2015).
Neftci, E. et al. Synthesizing cognition in neuromorphic electronic systems. Proc. Natl Acad. Sci. USA 110, 3468–3476 (2013).
Friedmann, S., Fremaux, N., Schemmel, J., Gerstner, W. & Meier, K. Reward-based learning under hardware constraints-using a RISC processor embedded in a neuromorphic substrate. Front. Neurosci. 7, 160 (2013).
Hassabis, D., Kumaran, D., Summerfield, C. & Botvinick, M. Neuroscience-inspired artificial intelligence. Neuron 95, 245–258 (2017).
Courbariaux, M., Bengio, Y. & David, J.-P. Training deep neural networks with low precision multiplications. Preprint at https://arxiv.org/abs/1412.7024 (2014).
Detorakis, G. et al. Neural and synaptic array transceiver: a brain-inspired computing framework for embedded learning. Front. Neurosci. 12, 583 (2018).
Liu, S. C. & Delbruck, T. Neuromorphic sensory systems. Curr. Opin. Neurobiol. 20, 288–295 (2010).
This work was supported by the Intramural Research Program of the National Institute of Mental Health (ZIA MH002928-01), and by the National Science Foundation under grant 1640081.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Neftci, E.O., Averbeck, B.B. Reinforcement learning in artificial and biological systems. Nat Mach Intell 1, 133–143 (2019). https://doi.org/10.1038/s42256-019-0025-4
Proceedings of the National Academy of Sciences (2021)
Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy
Machine Learning-Based Cognitive Position and Force Controls for Power-Assisted Human–Robot Collaborative Manipulation
Intelligence matters for stochastic feedback processing during sequence learning in adolescents and young adults