Efficient energy distribution in smart grids is an important problem driven by the need to
manage increasing power consumption across the globe. This problem has been studied in …

We revisit the standard formulation of tabular actor-critic algorithm as a two time-scale
stochastic approximation with value function computed on a faster time-scale and policy …

Reinforcement learning (RL) is an exciting area within the domain of Markov Decision
Processes (MDPs) in which the underlying optimization problem is solved either in a …

In this letter, we derive a generalization of the Speedy Q-learning (SQL) algorithm that was
proposed in the Reinforcement Learning (RL) literature to handle slow convergence of …

A large number of systems are well-modeled by Markov Decision Processes (MDPs). In
particular, certain wireless communication networks and biological networks admit such …

Q-learning and SARSA with $\epsilon $-greedy exploration are leading reinforcement
learning methods. Their tabular forms converge to the optimal Q-function under reasonable …

For a Reinforcement Learning (RL) algorithm to be practically useful, the policy it estimates
in the limit must be superior to the initial guess, at least on average. In this work, we show …

Q-learning and SARSA with ϵ-greedy exploration are leading reinforcement learning
methods. Their tabular forms converge to the optimal Q-function under reasonable …

Q-learning and SARSA (0) with $\epsilon $-greedy exploration are leading reinforcement
learning methods, and their tabular forms converge to the optimal Q-function under …