Abstract. Developing superior artificial board-game players is a widelystudied area of Artificial Intelligence. Among the most challenging games is the Asian game of Go, which, des...
This paper describes a method for hierarchical reinforcement learning in which high-level policies automatically discover subgoals, and low-level policies learn to specialize for ...
We present Policy Gradient Actor-Critic (PGAC), a new model-free Reinforcement Learning (RL) method for creating limited-memory stochastic policies for Partially Observable Markov ...
Abstract. Learning to act in an unknown partially observable domain is a difficult variant of the reinforcement learning paradigm. Research in the area has focused on model-free m...