Volume 16 · Number 1 · Pages 060–062
Maximization of Future Internal States?

Robert Lowe

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Abstract

Open peer commentary on the article “Foresight Rather than Hindsight? Future State Maximization As a Computational Interpretation of Heinz von Foerster’s Ethical Imperative” by Hannes Hornischer, Simon Plakolb, Georg Jäger & Manfred Füllsack. Abstract: The target article outlines a Future-State-Maximization (FSX) approach whose focus on “rewarding” actions that lead to increased action possibilities serves as an alternative to standard value-based learning approaches. In my commentary, I discuss how internal states might shape future action possibilities. Specifically, the notion of allostasis is discussed in relation to how physiological (internal variable) regulation may enable or constrain future action spaces.

Handling Editor: Alexander Riegler

Citation

Lowe R. (2020) Maximization of future internal states? Constructivist Foundations 16(1): 060–062. https://constructivist.info/16/1/060

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References

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Comments: 1

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Comment by Hernandez Cerezo Sergio · 15 Apr 2021
In our paper http://arxiv.org/abs/1803.05049 we discuss the need of using a reward asssociated with each state to act as a proxy for survival probabilities.
We played with using the energy and health levels (in the range 0–1) of the agent so the reward of an state is energy_level * health_level. This produces a behaviour that not only maximize future state diversity but also their rewards, so the agent chooses actions that increase future freedom of action (exploration) and overall probabilities of surviving afterwards (exploitation).
In adition to those “intrinsic rewards” of the agent, one can add new custom external rewards -like picking a rock with a hook giving you an extra reward- to push the agent to perform any desired custom task, while keeping its surviving probabilities and freedom of action always high.
The pseudo-code referred in the article corrrespond to a simplified version of the algorithm where rewards are not fully used. In our paper there is a “complete” version of the algorithm (see “4.3 Pseudo-code”).