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Cognitive Computation

, Volume 10, Issue 2, pp 296–306 | Cite as

A Brain-Inspired Decision Making Model Based on Top-Down Biasing of Prefrontal Cortex to Basal Ganglia and Its Application in Autonomous UAV Explorations

  • Feifei Zhao
  • Yi Zeng
  • Guixiang Wang
  • Jun Bai
  • Bo Xu
Article

Abstract

Decision making is a fundamental ability for intelligent agents (e.g., humanoid robots and unmanned aerial vehicles). During decision making process, agents can improve the strategy for interacting with the dynamic environment through reinforcement learning. Many state-of-the-art reinforcement learning models deal with relatively smaller number of state-action pairs, and the states are preferably discrete, such as Q-learning and Actor-Critic algorithms. While in practice, in many scenario, the states are continuous and hard to be properly discretized. Better autonomous decision making methods need to be proposed to handle these problems. Inspired by the mechanism of decision making in human brain, we propose a general computational model, named as prefrontal cortex-basal ganglia (PFC-BG) algorithm. The proposed model is inspired by the biological reinforcement learning pathway and mechanisms from the following perspectives: (1) Dopamine signals continuously update reward-relevant information for both basal ganglia and working memory in prefrontal cortex. (2) We maintain the contextual reward information in working memory. This has a top-down biasing effect on reinforcement learning in basal ganglia. The proposed model separates the continuous states into smaller distinguishable states, and introduces continuous reward function for each state to obtain reward information at different time. To verify the performance of our model, we apply it to many UAV decision making experiments, such as avoiding obstacles and flying through window and door, and the experiments support the effectiveness of the model. Compared with traditional Q-learning and Actor-Critic algorithms, the proposed model is more biologically inspired, and more accurate and faster to make decision.

Keywords

Prefrontal cortex Working memory Basal ganglia Dopamine system Brain-inspired decision making model 

Notes

Acknowledgments

This study was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB02060007), and Beijing Municipal Commission of Science and Technology (Z161100000216124). We would like to thank all the anonymous reviewers for all the constructive comments, which enables this paper to be with much better shape.

Compliance with Ethical Standards

Conflict of Interests

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants performed by any of the authors.

References

  1. 1.
    Botvinick MM. Hierarchical reinforcement learning and decision making. Curr Opin Neurobiol. 2012;22(6): 956–962.CrossRefPubMedGoogle Scholar
  2. 2.
    Lee D, Seo H, Jung MW. Neural basis of reinforcement learning and decision making. Ann Rev Neurosci. 2012;35(1):287–308.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Humphrys M. Action selection methods using reinforcement learning. Proceedings of the International Conference on Simulation of Adaptive Behavior; 1996. p. 135–144.Google Scholar
  4. 4.
    Arel I. Theoretical foundations of artificial general intelligence, chapter deep reinforcement learning as foundation for artificial general Intelligence:89–102. 2012.Google Scholar
  5. 5.
    Mnih V, Kavukcuoglu K, Silver D, Graves A, Antonoglou I, Wierstra D, Riedmiller M. Playing atari with deep reinforcement learning. 2013. arXiv:1312.5602.
  6. 6.
    Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G, Petersen S, Beattie C, Sadik A, Antonoglou I, King H, Kumaran D, Wierstra D, Legg S, Hassabis D. Human-level control through deep reinforcement learning. Nature. 2015;518:529–533.CrossRefPubMedGoogle Scholar
  7. 7.
    Hearn RA, Granger RH. Learning hierarchical representations and behaviors. Association for the Advancement of Artificial Intelligence. 2008. Google Scholar
  8. 8.
    Schultz W, Dickinson A. Neuronal coding of prediction errors. Ann Rev Neurosci. 2000;23:473–500.CrossRefPubMedGoogle Scholar
  9. 9.
    Alexander GE, Crutcher MD. Functional architecture of basal ganglia circuits Neural substrates of parallel processing. Trends Neurosci. 1990;13(7):266–271.CrossRefPubMedGoogle Scholar
  10. 10.
    Gerfen CR. The neostriatal mosaic: multiple levels of compartmental organization in the basal ganglia. J Neural Transm Suppl. 1992;36(4):43–59.PubMedGoogle Scholar
  11. 11.
    Joel D, Weiner I. The organization of the basal ganglia-thalamocortical circuits: open interconnected rather than closed segregated. Neuroscience. 1994;63(2):363–379.CrossRefPubMedGoogle Scholar
  12. 12.
    Joel D, Weiner I. The connections of the primate subthalamic nucleus: indirect pathways and the open-interconnected scheme of basal ganglia-thalamocortical circuitry. Brain Res Rev. 1997;23:62–78.CrossRefPubMedGoogle Scholar
  13. 13.
    Parent A. Extrinsic connections of the basal ganglia. Trends Neurosci. 1990;13(7):254–258.CrossRefPubMedGoogle Scholar
  14. 14.
    Joel D, Weiner I. The connections of the dopaminergic system with the striatum in rats and primates: an analysis with respect to the functional and compartmental organization of the striatum. Neuroscience. 2000;96(3): 451–474.CrossRefPubMedGoogle Scholar
  15. 15.
    Schultz W, Apicella P, Scarnati E, Ljungberg T. Neuronal activity in monkey ventral striatum related to the expectation of reward. J Neurosci. 1992;12(12):4595–4610.CrossRefPubMedGoogle Scholar
  16. 16.
    O’Reilly RC, Frank MJ. Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia. Neural Comput. 2006;18(2):283–328.CrossRefPubMedGoogle Scholar
  17. 17.
    Frank MJ, Claus ED. Anatomy of a decision: striato-orbitofrontal interactions in reinforcement learning, decision making, and reversal. Psychol Rev. 2006;113(2):300–326.CrossRefPubMedGoogle Scholar
  18. 18.
    Dayan P, Daw ND. Decision theory, reinforcement learning, and the brain. Cogn Affect Behav Neurosci. 2008; 8(4):429–453.CrossRefPubMedGoogle Scholar
  19. 19.
    Shadlen MN, Newsome WT. Motion perception: seeing and deciding. Proc Natl Acad Sci. 1996;93(2):628–633.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Karni E. A theory of bayesian decision making with action-dependent subjective probabilities. Econ Theory. 2011; 48(1):125–146.CrossRefGoogle Scholar
  21. 21.
    Mnih V, Badia AP, Mirza M, Graves A, Lillicrap TP, Harley T, Silver D, Kavukcuoglu K. Asynchronous methods for deep reinforcement learning. Proceedings of the 33th international conference on machine learning; 2016. p. 1928–1937.Google Scholar
  22. 22.
    Timothy P, Lillicrap J, Hunt J, Pritzel A, Heess N, Erez T, Tassa Y, Silver D, Wierstra D. Continuous control with deep reinforcement learning. 2015. arXiv:1509.02971.
  23. 23.
    Hasselt HV, Guez A, Silver D. Deep reinforcement learning with double q-learning. Proceedings of the 30th AAAI conference on artificial intelligence; 2016.Google Scholar
  24. 24.
    Nair A, Srinivasan P, Blackwell S, Alcicek C, Fearon R, De Maria A, Panneershelvam V, Suleyman M, Beattie C, Petersen S. Massively parallel methods for deep reinforcement learning. 2015. arXiv:1507.04296.
  25. 25.
    Barto AG, Mahadevan S. Recent advances in hierarchical reinforcement learning. Discrete Event Dyn Syst. 2003;13(1):41–77.CrossRefGoogle Scholar
  26. 26.
    Morimoto J, Doyayy K. Hierarchical reinforcement learning of low-dimensional subgoals and high-dimensional trajectories. Proceedings of the 5th International Conference on Neural Information Processing; 1998. p. 850–853.Google Scholar
  27. 27.
    Smart WD, Kaelbling LP. Practical reinforcement learning in continuous spaces. Proceedings of the 17th International Conference on Machine Learning; 2000. p. 903–910.Google Scholar
  28. 28.
    Lazaric A, Restelli M, Bonarini A. Reinforcement learning in continuous action spaces through sequential monte carlo methods. Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems; 2007. p. 833–840.Google Scholar
  29. 29.
    Joel D, Niv Y, Ruppin E. Actor-ccritic models of the basal ganglia: new anatomical and computational perspectives. Neural Netw. 2002;15(4):535–547.CrossRefPubMedGoogle Scholar
  30. 30.
    Frémaux N, Sprekeler H, Gerstner W. Reinforcement learning using a continuous time actor-critic framework with spiking neurons. PLOS Comput Biology. 2013;9(4):1–21.CrossRefGoogle Scholar
  31. 31.
    Ellaithy K, Bogdan M. A reinforcement learning framework for spiking networks with dynamic synapses. Comput Intell Neuroscience. 2011;2011(3):713–750.Google Scholar
  32. 32.
    Kim HF, Hikosaka O. Parallel basal ganglia circuits for voluntary and automatic behaviour to reach rewards. Brain. 2015;138(7):1776–1800.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Berns GS, Sejnowski TJ. A computational model of how the basal ganglia produce sequences. J Cogn Neurosci. 1998;10(1):108–121.CrossRefPubMedGoogle Scholar
  34. 34.
    Kumaravelu K, Brocker DT, Grill WM. A biophysical model of the cortex-basal ganglia-thalamus network in the 6-ohda lesioned rat model of parkinson’s disease. J Comput Neurosci. 2016;40(2):207–229.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Debnath S, Nassour J. Extending cortical-basal inspired reinforcement learning model with success-failure experience. Proceedings of 4th IEEE International Conference on Development and Learning and on Epigenetic Robotics; 2014. p. 293–298.Google Scholar
  36. 36.
    Vijay R, John N. Tsitsiklis Konda actor-critic algorithms. SLAM J Control Optim. 2003;42(4):1143–1166.CrossRefGoogle Scholar
  37. 37.
    Grondman I, Busoniu L, Lopes G, Babuska R. A survey of actor-critic reinforcement learning Standard and natural policy grdients. IEEE Trans Syst Man Cybern. 2012;42(6):1291–1307.CrossRefGoogle Scholar
  38. 38.
    Sutton RS, Barto AG. 1998. Reinforcement Learning: an introduction, chapter the reinforcement learning problem:70–71.Google Scholar
  39. 39.
    Sutton RS, Barto AG. Reinforcement Learning: an introduction, chapter temporal-difference learning:188–190. 1998.Google Scholar
  40. 40.
    Sutton RS, Barto AG. Reinforcement Learning: an introduction, chapter evaluative feedback:40–42. 1998.Google Scholar
  41. 41.
    Sutton RS, Barto AG. Reinforcement Learning: an introduction, chapter temporal-difference learning:185–186. 1998.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghaiChina
  3. 3.University of Chinese Academy of SciencesBeijingChina

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