A hierarchical system of learning automata that can learn the globally optimal path
Information Sciences: an International Journal
Learning automata: an introduction
Learning automata: an introduction
The Convergence of TD(λ) for General λ
Machine Learning
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Some studies in machine learning using the game of checkers
IBM Journal of Research and Development
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Animals increase or decrease their future tendency of emitting an action based on whether performing such action has, in the past, resulted in positive or negative reinforcement. An analysis in the companion paper [Zhang, J. (2009). Adaptive learning via selectionism and Bayesianism. Part I: Connection between the two. Neural Networks, 22(3), 220-228] of such selectionist style of learning reveals a resemblance between its ensemble-level dynamics governing the change of action probability and Bayesian learning where evidence (in this case, reward) is distributively applied to all action alternatives. Here, this equivalence is further explored in solving the temporal credit-assignment problem during the learning of an action sequence (''operant chain''). Naturally emerging are the notion of secondary (conditioned) reinforcement predicting the average reward associated with a stimulus, and the notion of actor-critic architecture involving concurrent learning of both action probability and reward prediction. While both are consistent with solutions provided by contemporary reinforcement learning theory (Sutton & Barto, 1998) for optimizing sequential decision-making under stationary Markov environments, we investigate the effect of action learning on reward prediction when both are carried out concurrently in any on-line scheme.