<abstract xmlns="http://www.w3.org/1999/xhtml"><p> One of the essential aspect in biological agents is dynamic stability. This aspect, called homeostasis, is widely discussed in ethology, neuroscience and during the early stages of artificial intelligence. Ashby’s homeostats are general-purpose learning machines for stabilizing essential variables of the agent in the face of general environments. However, despite their generality, the original homeostats couldn’t be scaled because they searched their parameters randomly. In this paper, first we re-define the objective of homeostats as the maximization of a multi-step survival probability from the view point of sequential decision theory and probabilistic theory. Then we show that this optimization problem can be treated by using reinforcement learning algorithms with special agent architectures and theoretically-derived intrinsic reward functions. Finally we empirically demonstrate that agents with our architecture automatically learn to survive in a given environment, including environments with visual stimuli. Our survival agents can learn to eat food, avoid poison and stabilize essential variables through theoretically-derived single intrinsic reward formulations.</p></abstract>

One of the essential aspect in biological agents is dynamic stability. This aspect, called homeostasis, is widely discussed in ethology, neuroscience and during the early stages of artificial intelligence. Ashby’s homeostats are general-purpose learning machines for stabilizing essential variables of the agent in the face of general environments. However, despite their generality, the original homeostats couldn’t be scaled because they searched their parameters randomly. In this paper, first we re-define the objective of homeostats as the maximization of a multi-step survival probability from the view point of sequential decision theory and probabilistic theory. Then we show that this optimization problem can be treated by using reinforcement learning algorithms with special agent architectures and theoretically-derived intrinsic reward functions. Finally we empirically demonstrate that agents with our architecture automatically learn to survive in a given environment, including environments with visual stimuli. Our survival agents can learn to eat food, avoid poison and stabilize essential variables through theoretically-derived single intrinsic reward formulations.

Homeostatic Agent for General Environment

Journal of Artificial General Intelligence

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Homeostatic Agent for General Environment"}

One of the essential aspect in biological agents is dynamic stability. This aspect, called homeostasis, is widely discussed in ethology, neuroscience and...

Homeostatic Agent for General Environment

Data publikacji: 07 mar 2018

Zakres stron: 1 - 22

Otrzymano: 27 mar 2017

Przyjęty: 11 maj 2017

DOI: https://doi.org/10.1515/jagi-2017-0001

Słowa kluczowe
Homeostat, Reward, Reinforcement Learning, Survival

© by Naoto Yoshida

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Homeostatic Agent for General Environment

Data publikacji: 07 mar 2018

Zakres stron: 1 - 22

Otrzymano: 27 mar 2017

Przyjęty: 11 maj 2017

DOI: https://doi.org/10.1515/jagi-2017-0001

Słowa kluczoweHomeostat, Reward, Reinforcement Learning, Survival

© by Naoto Yoshida

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
Homeostat, Reward, Reinforcement Learning, Survival