… have proposed reinforcement learning methods that obtain advantages in learning by using
temporally extended actions, or macro-actions, but none has carefully analyzed what these …

… empirically analyzed the performance of automated stock trading based on deep reinforcement
learning … We conducted empirical analysis in three ways to determine whether it is …

… analysis of Expected Sarsa, a variation on Sarsa, the classic onpolicy temporal-difference
method for model-free reinforcement learning. Expected Sarsa exploits knowledge about …

… reinforcement learning tasks. While both classes of methods benefit from independent theoretical
analyses, … We conduct an empirical study to examine the strengths and weaknesses of …

… , and we believe that by identifying, replicating and solving these challenges, reinforcement
learning can be more readily used to solve many of these important real-world problems. …

… learning. This paper undertakes a detailed examination of average reward reinforcement
learning… , adaptive control, learning automata, and reinforcement learning. A general optimality …

… Computational models of reinforcement learning have played an important role in understanding
learning and decision making behavior, as well as the neural mechanisms underlying …

… motivation [10, 13, 12] and has shown empirical success in developmental robotics [1]. An …
on empirical measures of learning progress to drive exploration in reinforcement learning [17, …

… Reinforcement learning suffers scalability problems due to the state space explosion and …
domain knowledge into reinforcement learning. Theoretical and empirical analysis of this …

… We present a Reinforcement Learning (RL) algorithm based on policy iteration for solving
average … We also present a convergence analysis of the algorithm via an ordinary differential …