Hierarchical reinforcement learning using a modular fuzzy model for multi-agent problem

Reinforcement learning is a promising approach to realize intelligent agent such as autonomous mobile robots. In order to apply the reinforcement learning to actual sized problem, the "curse of dimensionality" problem in partition of sensory states should be avoided maintaining computational efficiency. The paper describes a hierarchical modular reinforcement learning that Profit Sharing learning algorithm is combined with Q-Learning reinforcement learning algorithm hierarchically in multi-agent pursuit environment. As the model structure for such huge problem, I propose a modular fuzzy model extending SIRMs architecture. Through numerical experiments, I found that the proposed method has good convergence property of learning compared with the conventional algorithms.

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Investment decision making by using fuzzy candlestick pattern and genetic algorithm

Lee

Liaw

Hsu

2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

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This paper proposed an approach to extract fuzzy candlestick patterns from financial time series and select a set of patterns for investment decision making. The candlestick chart in stock market is a widely used technical analysis model. The investor observes the candlestick chart and makes investment decisions by identifying patterns in the chart. We use fuzzy linguistic variables to model candlestick chart and extract patterns from the chart. A Genetic algorithm based approach is used to select a set of extracted pattern as the background knowledge in the system for investment decision making. The advantage of the proposed approach is the investment knowledge is comprehensible, editable, and visible. The user can set different range of historical financial time series to extract and select different set of patterns. The experimental results shows that the investment decisions based on selected fuzzy patterns have better investment performance than using original non-fuzzy patterns.

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Hierarchical reinforcement learning using a modular fuzzy model for multi-agent problem

Cited by 4 publications

References 11 publications

A study on hierarchical modular reinforcement learning for multi-agent pursuit problem based on relative coordinate states

A study on hierarchical modular reinforcement learning for multi-agent pursuit problem based on relative coordinate states

A study on multi-agent reinforcement learning problem based on hierarchical modular fuzzy model

Investment decision making by using fuzzy candlestick pattern and genetic algorithm

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