Variance-penalized Markov decision processes
Mathematics of Operations Research
Asynchronous Stochastic Approximations
SIAM Journal on Control and Optimization
The O.D. E. Method for Convergence of Stochastic Approximation and Reinforcement Learning
SIAM Journal on Control and Optimization
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Reinforcement Learning
Average-Reward Reinforcement Learning for Variance Penalized Markov Decision Problems
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Q-Learning for Risk-Sensitive Control
Mathematics of Operations Research
The Journal of Machine Learning Research
Introduction to Probability Models, Ninth Edition
Introduction to Probability Models, Ninth Edition
A risk-sensitive approach to total productive maintenance
Automatica (Journal of IFAC)
Risk-sensitive reinforcement learning applied to control under constraints
Journal of Artificial Intelligence Research
Stochastic policy search for variance-penalized semi-Markov control
Proceedings of the Winter Simulation Conference
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Reinforcement Learning (RL) is a simulation-based technique useful in solving Markov decision processes if their transition probabilities are not easily obtainable or if the problems have a very large number of states. We present an empirical study of (i) the effect of step-sizes (learning rules) in the convergence of RL algorithms, (ii) stochastic shortest paths in solving average reward problems via RL, and (iii) the notion of survival probabilities (downside risk) in RL. We also study the impact of step sizes when function approximation is combined with RL. Our experiments yield some interesting insights that will be useful in practice when RL algorithms are implemented within simulators.