Suggested further readings
Contents
Suggested further readings¶
Overview¶
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.
Links to neuroscience¶
Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275(5306), 1593-1599.
Daw, N. D., Niv, Y., & Dayan, P. (2005). Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nature neuroscience, 8(12), 1704-1711.
Dayan, P., & Niv, Y. (2008). Reinforcement learning: the good, the bad and the ugly. Current opinion in neurobiology, 18(2), 185-196.
Wang, J. X., Kurth-Nelson, Z., Kumaran, D., Tirumala, D., Soyer, H., Leibo, J. Z., … & Botvinick, M. (2018). Prefrontal cortex as a meta-reinforcement learning system. Nature neuroscience, 21(6), 860-868.
Mattar, M. G., & Daw, N. D. (2018). Prioritized memory access explains planning and hippocampal replay. Nature neuroscience, 21(11), 1609-1617.
State of the art¶
Dabney, W., Kurth-Nelson, Z., Uchida, N., Starkweather, C. K., Hassabis, D., Munos, R., & Botvinick, M. (2020). A distributional code for value in dopamine-based reinforcement learning. Nature, 577(7792), 671-675.
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., … & Petersen, S. (2015). Human-level control through deep reinforcement learning. nature, 518(7540), 529-533.
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., … & Dieleman, S. (2016). Mastering the game of Go with deep neural networks and tree search. nature, 529(7587), 484-489.