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JRM Vol.27 No.1 pp. 57-63
doi: 10.20965/jrm.2015.p0057
(2015)

Paper:

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Self-Generation of Reward by Moderate-Based Index for Senor Inputsvspace

Kentarou Kurashige and Kaoru Nikaido

Department of Information and Electronic Engineering, Muroran Institute of Technology
27-1 Mizumoto-cho, Muroran, Hokkaido 050-8585, Japan

Received:
August 17, 2014
Accepted:
December 19, 2014
Published:
February 20, 2015
Creative Commons License
Keywords:
reward generation, reinforcement learning, pleasure and pain, robot-human interaction, inborn index and immunity evaluation
Abstract
Moderate-based reward generator
In conventional reinforcement learning, a reward function influences the learning results, and therefore, the reward function is very important. To design this function considering a task, knowledge of reinforcement learning is required. In addition to this, a reward function must be designed for each task. These requirements make the design of a reward function unfeasible. We focus on this problem and aim at realizing a method to generate a reward without the design of a special reward function. In this paper, we propose a universal evaluation for sensor inputs, which is independent of a task and is modeled on the basis of the indicator of pleasure and pain in biological organisms. This evaluation estimates the trend of sensor inputs based on the ease of input prediction. Instead of the design of a reward function, our approach assists a human being in learning how to interact with an agent and teaching it his/her demand. We recruited a research participant and attempted to solve the path planning problem. The results show that a participant can teach an agent his/her demand by interacting with the agent and the agent can generate an adaptive route by interacting with the participant and the environment.
Cite this article as:
K. Kurashige and K. Nikaido, “Self-Generation of Reward by Moderate-Based Index for Senor Inputsvspace,” J. Robot. Mechatron., Vol.27 No.1, pp. 57-63, 2015.
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