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Efficient exploration through active learning for value function approximation in reinforcement learning.

Takayuki Akiyama¹, Hirotaka Hachiya, Masashi Sugiyama

¹Department of Computer Science, Tokyo Institute of Technology, 2-12-1 O-okayama, Meguro-ku, Tokyo 152-8552, Japan. akiyama@sg.cs.titech.ac.jp

Neural Networks : the Official Journal of the International Neural Network Society

|January 19, 2010

Summary

This summary is machine-generated.

Designing effective sampling policies is crucial for reinforcement learning control. This study introduces active policy iteration (API) for efficient exploration, especially when reward sampling is costly, improving reinforcement learning outcomes.

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Area of Science:

Reinforcement Learning
Machine Learning
Robotics

Background:

Effective sampling policies are essential for optimal control in reinforcement learning.
The least-squares policy iteration (LSPI) framework can integrate statistical active learning for linear regression.

Purpose of the Study:

To propose a novel method for designing sampling policies that enhance exploration efficiency in reinforcement learning.
To address challenges in reinforcement learning where the cost of sampling immediate rewards is high.

Main Methods:

Leveraging the least-squares policy iteration (LSPI) framework to incorporate statistical active learning.
Developing an active policy iteration (API) approach for optimized sampling policy design.

Main Results:

Demonstrated that LSPI can utilize active learning methods for linear regression.
Showcased the effectiveness of the proposed active policy iteration (API) method through simulations.

Conclusions:

The active policy iteration (API) method provides an efficient strategy for exploration in reinforcement learning.
The proposed method is particularly beneficial in scenarios with high sampling costs for immediate rewards, as shown in batting robot simulations.