Simple Reinforcement Learning for Small-Memory Agent

In this paper, we propose Simple Reinforcement Learning for a reinforcement learning agent that has small memory. In the real world, learning is difficult because there are an infinite number of states and actions that need a large number of stored memories and learning times. To solve a problem, es...

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Bibliographic Details
Main Authors: Notsu, A., Honda, K., Ichihashi, H., Komori, Y.
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Games
Intelligent systems
Learning
Machine learning
Markov processes
Memory management
Q-learning
Reinforcement learning
State-action set categorize
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Summary:In this paper, we propose Simple Reinforcement Learning for a reinforcement learning agent that has small memory. In the real world, learning is difficult because there are an infinite number of states and actions that need a large number of stored memories and learning times. To solve a problem, estimated values are categorized as "GOOD" or "NO GOOD" in the reinforcement learning process. Additionally, the alignment sequence of estimated values is changed because they are regarded as an important sequence themselves. We conducted some simulations and observed the influence of our methods. Several simulation results show no bad influence on learning speed.
DOI:10.1109/ICMLA.2011.127