Technical Note: \cal Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
SIAM Journal on Control and Optimization
Policy Gradient in Continuous Time
The Journal of Machine Learning Research
Infinite-horizon policy-gradient estimation
Journal of Artificial Intelligence Research
Experiments with infinite-horizon, policy-gradient estimation
Journal of Artificial Intelligence Research
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Reinforcement Learning (RL) is analyzed here as a tool for control system optimization. State and action spaces are assumed to be continuous. Time is assumed to be discrete, yet the discretization may be arbitrarily fine. It is shown here that stationary policies, applied by most RL methods, are improper in control applications, since for fine time discretization they can not assure bounded variance of policy gradient estimators. As a remedy to that difficulty, we propose the use of piecewise non-Markov policies. Policies of this type can be optimized by means of most RL algorithms, namely those based on likelihood ratio.