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Exploitation-oriented learning (XoL) is a novel approach to goal-directed learning from interaction. Reinforcement learning is much more focused on learning and ensures optimality in Markov decision process (MDP) environments, XoL involves learning a rational policy that obtains rewards continuously and very quickly. PS-r*, a form of XoL, involves learning a useful rational policy not inferior to the random walk in the partially observed Markov decision process (POMDP) where reward types number one. PS-r*, however, requires O(MN2) memory where N is the number of sensory input types and M is an action. We propose PS-r#for learning a useful rational policy in the POMDP using O(MN) memory. PS-r#effectiveness is confirmed in numerical examples.

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When multiple agents learn a task simultaneously in an environment, the learning results often become unstable. This problem is known as the concurrent learning problem and to date, several methods have been proposed to resolve it. In this paper, we propose a new method that incorporates expected failure probability (EFP) into the action selection strategy to give agents a kind of mutual adaptability. The effectiveness of the proposed method is confirmed using Keepaway task.

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