Psychology Dictionary of ArgumentsHome
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| Reinforcement learning: Reinforcement learning (RL) is a type of machine learning that allows an agent to learn how to behave in an environment by trial and error. See also Learning, Machine learning, Artificial Intelligence._____________Annotation: The above characterizations of concepts are neither definitions nor exhausting presentations of problems related to them. Instead, they are intended to give a short introduction to the contributions below. – Lexicon of Arguments. | |||
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Nick Bostrom on Reinforcement Learning - Dictionary of Arguments
I 230 Reinforcement learning/superintelligence/values/Bostrom: Often, the learning algorithm involves the gradual construction of some kind of evaluation function, which assigns values to states, state-action pairs, or policies. Problem: The evaluation function, which is continuously updated in light of experience, could be regarded as incorporating a form of learning about value. However, what is being learned is not new final values but increasingly accurate estimates of the instrumental values of reaching particular states (or of taking particular actions in particular states, or of following particular policies). Insofar as a reinforcement-learning agent can be described as having a final goal, that goal remains constant: to maximize future reward. And reward consists of specially designated percepts received from the environment. Therefore, the wireheading syndrome remains a likely outcome in any reinforcement agent that develops a world model sophisticated enough to suggest this alternative way of maximizing reward. >Values/superintelligence/Bostrom._____________Explanation of symbols: Roman numerals indicate the source, arabic numerals indicate the page number. The corresponding books are indicated on the right hand side. ((s)…): Comment by the sender of the contribution. Translations: Dictionary of Arguments The note [Concept/Author], [Author1]Vs[Author2] or [Author]Vs[term] resp. "problem:"/"solution:", "old:"/"new:" and "thesis:" is an addition from the Dictionary of Arguments. If a German edition is specified, the page numbers refer to this edition. |
Bostrom I Nick Bostrom Superintelligence. Paths, Dangers, Strategies Oxford: Oxford University Press 2017 |
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> Counter arguments in relation to Reinforcement Learning
