Reinforcement learning using a continuous time actor-critic framework with spiking neurons

PLoS Comput Biol. 2013 Apr;9(4):e1003024. doi: 10.1371/journal.pcbi.1003024. Epub 2013 Apr 11.

Abstract

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Action Potentials / physiology
  • Animals
  • Behavior, Animal
  • Computational Biology / methods
  • Computer Simulation
  • Learning / physiology*
  • Maze Learning
  • Models, Neurological
  • Nerve Net / physiology
  • Neurons / physiology*
  • Rats
  • Reinforcement, Psychology*
  • Reproducibility of Results
  • Reward
  • Time Factors

Grant support

This work was supported by Project FP7-243914(Brain-I-Nets) of the European Community's Seventh Framework Program (http://cordis.europa.eu/fp7/home_en.html) and a Sinergia grant of the Swiss National Science Foundation (SNF, http://www.snf.ch, grant no. CRSIK2\_122697, State representations in reward-based learning). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.