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disadvantages of model-based and model-free reinforcement learning in a robotics
neuro-inspired cognitive architecture. In: Biologically Inspired Cognitive Architectures BICA 2015, (Lyon, France), pp. 178–184 (2015)
10. Renaudo, E., Girard, B., Chatila, R., Khamassi, M.: Which criteria for
autonomously shifting between goal-directed and habitual behaviors in robots?
In: 5th International Conference on Development and Learning and on Epigenetic
Robotics (ICDL-EPIROB), pp. 254–260. (Providence, RI, USA) (2015)
11. Gat, E.: On three-layer architectures. In: Artificial Intelligence and Mobile Robots.
MIT Press (1998)
12. Alami, R., Chatila, R., Fleury, S., Ghallab, M., Ingrand, F.: An architecture for
autonomy. IJRR J. 17, 315–337 (1998)
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Press, Cambridge (1998)
14. Viejo, G., Khamassi, M., Brovelli, A., Girard, B.: Modelling choice and reaction
time during arbitrary visuomotor learning through the coordination of adaptive
working memory and reinforcement learning. Front. Behav. Neurosci. 9(225) (2015)
15. Powell, T., Sammut-Bonnici, T.: Pareto Analysis (2015)
16. Quigley, M., et al.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software (2009)
17. Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping
with Rao-blackwellized particle filters. Trans. Rob. 23, 34–46 (2007)
18. Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature
518, 529–533 (2015)
19. Dromnelle, R., Girard, B., Renaudo, E., Chatila, R., Khamassi, M.: Coping
with the variability in humans reward during simulated human-robot interactions
through the coordination of multiple learning strategies. In: The 29th IEEE International Conference on Robot & Human Interactive Communication (2020)
79
7. Daw, N., Niv, Y., Dayan, P.: Uncertainty-based competition between prefrontal
and dorsolateral striatal systems for behavioral control. Nat. Neurosci. 8(12), 1704–
1711 (2005)
8. Khamassi, M., Humphries, M.: Integrating cortico-limbic-basal ganglia architectures for learning model-based and model-free navigation strategies. Front. Behav.
Neurosci. 6, 79 (2012)
9. Renaudo, E., Girard, B., Chatila, R., Khamassi, M.: Respective advantages and
disadvantages of model-based and model-free reinforcement learning in a robotics
neuro-inspired cognitive architecture. In: Biologically Inspired Cognitive Architectures BICA 2015, (Lyon, France), pp. 178–184 (2015)
10. Renaudo, E., Girard, B., Chatila, R., Khamassi, M.: Which criteria for
autonomously shifting between goal-directed and habitual behaviors in robots?
In: 5th International Conference on Development and Learning and on Epigenetic
Robotics (ICDL-EPIROB), pp. 254–260. (Providence, RI, USA) (2015)
11. Gat, E.: On three-layer architectures. In: Artificial Intelligence and Mobile Robots.
MIT Press (1998)
12. Alami, R., Chatila, R., Fleury, S., Ghallab, M., Ingrand, F.: An architecture for
autonomy. IJRR J. 17, 315–337 (1998)
13. Sutton, R.S., Barto, A.G.: Introduction to Reinforcement Learning, 1st edn. MIT
Press, Cambridge (1998)
14. Viejo, G., Khamassi, M., Brovelli, A., Girard, B.: Modelling choice and reaction
time during arbitrary visuomotor learning through the coordination of adaptive
working memory and reinforcement learning. Front. Behav. Neurosci. 9(225) (2015)
15. Powell, T., Sammut-Bonnici, T.: Pareto Analysis (2015)
16. Quigley, M., et al.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software (2009)
17. Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping
with Rao-blackwellized particle filters. Trans. Rob. 23, 34–46 (2007)
18. Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature
518, 529–533 (2015)
19. Dromnelle, R., Girard, B., Renaudo, E., Chatila, R., Khamassi, M.: Coping
with the variability in humans reward during simulated human-robot interactions
through the coordination of multiple learning strategies. In: The 29th IEEE International Conference on Robot & Human Interactive Communication (2020)