H-Learning: A Reinforcement Learning Method for Optimizing Undiscounted Average Reward

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Reinforcement learning in robotics: A survey (2013, methodology): “H-learning (Tadepalli and Ok, 1994) is a related method that estimates a model of the transition probabilities and the reward function in order to perform updates that are reminiscent of value iteration.”
Average reward reinforcement learning: Foundations, algorithms, and empirical results (2004, background): “Recent work by Tadepalli and Ok ( Tadepalli & Ok, 1994 ) also supports both these conclusions.”
Average Reward Reinforcement Learning: Foundations, Algorithms, and Empirical Results (2005, methodology): “Tadepalli and Ok [38] have undertaken a detailed study of a variant of this algorithm, which includes two modi cations.”
Reinforcement Learning: An Introduction (1998, background): “Ways to formulate tasks that are both continuing and undiscounted are the subject of current research (e.g., Mahadevan, 1996; Schwartz, 1993; Tadepalli and Ok, 1994).”
Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function (1996, methodology): “Recently, there has been grow-ing interest in Average-reward Reinforcement Learn-ing (ARL) (Schwartz, 1993; Singh, 1994; Mahadevan,1996; Boutilier & Puterman, 1995; Tadepalli & Ok,1994).”