Is supervised learning a subset of reinforcement learning?

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New to self-education in ML and struggling to parse some of the basic definitions, apologies if this question is too broad for this type of forum.

It seems like the definition of supervised learning is a subset of reinforcement learning, with a particular type of reward function that is based on labeled data (as opposed to other information in the environment). Is this an accurate portrayal?

machine-learning reinforcement-learning supervised-learning

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New to self-education in ML and struggling to parse some of the basic definitions, apologies if this question is too broad for this type of forum.

It seems like the definition of supervised learning is a subset of reinforcement learning, with a particular type of reward function that is based on labeled data (as opposed to other information in the environment). Is this an accurate portrayal?

machine-learning reinforcement-learning supervised-learning

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edited 1 hour ago

user20160

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up vote
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down vote

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up vote
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down vote

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New to self-education in ML and struggling to parse some of the basic definitions, apologies if this question is too broad for this type of forum.

It seems like the definition of supervised learning is a subset of reinforcement learning, with a particular type of reward function that is based on labeled data (as opposed to other information in the environment). Is this an accurate portrayal?

machine-learning reinforcement-learning supervised-learning

share|cite|improve this question

edited 1 hour ago

user20160

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asked 3 hours ago

allstar

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New to self-education in ML and struggling to parse some of the basic definitions, apologies if this question is too broad for this type of forum.

It seems like the definition of supervised learning is a subset of reinforcement learning, with a particular type of reward function that is based on labeled data (as opposed to other information in the environment). Is this an accurate portrayal?

machine-learning reinforcement-learning supervised-learning

machine-learning reinforcement-learning supervised-learning

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edited 1 hour ago

user20160

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asked 3 hours ago

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1235

share|cite|improve this question

edited 1 hour ago

user20160

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asked 3 hours ago

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share|cite|improve this question

edited 1 hour ago

user20160

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edited 1 hour ago

user20160

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edited 1 hour ago

user20160

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asked 3 hours ago

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asked 3 hours ago

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It's true that any supervised learning problem can be cast as an equivalent reinforcement learning problem: Let states correspond to the input data. Let actions correspond to predictions of the output. Define reward as the negative of the loss function used for supervised learning. Maximize expected reward. In contrast, reinforcement learning problems can't generally be cast as supervised learning problems. So, from this perspective, supervised learning problems are a subset of reinforcement learning problems.

But, trying to solve a supervised learning problem using a general reinforcement learning algorithm would be rather pointless; all this does is throw away structure that would have made the problem easier to solve. Various issues arise in reinforcement learning that aren't relevant to supervised learning. And, supervised learning can benefit from approaches that don't apply in the general reinforcement learning setting. So, although there are some common underlying principles and shared techniques between the fields, one doesn't typically see supervised learning discussed as a type of reinforcement learning.

References

Barto and Dietterich (2004). Reinforcement learning and its relationship to supervised learning.

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It's true that any supervised learning problem can be cast as an equivalent reinforcement learning problem: Let states correspond to the input data. Let actions correspond to predictions of the output. Define reward as the negative of the loss function used for supervised learning. Maximize expected reward. In contrast, reinforcement learning problems can't generally be cast as supervised learning problems. So, from this perspective, supervised learning problems are a subset of reinforcement learning problems.

But, trying to solve a supervised learning problem using a general reinforcement learning algorithm would be rather pointless; all this does is throw away structure that would have made the problem easier to solve. Various issues arise in reinforcement learning that aren't relevant to supervised learning. And, supervised learning can benefit from approaches that don't apply in the general reinforcement learning setting. So, although there are some common underlying principles and shared techniques between the fields, one doesn't typically see supervised learning discussed as a type of reinforcement learning.

References

Barto and Dietterich (2004). Reinforcement learning and its relationship to supervised learning.

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answered 1 hour ago

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up vote
4
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It's true that any supervised learning problem can be cast as an equivalent reinforcement learning problem: Let states correspond to the input data. Let actions correspond to predictions of the output. Define reward as the negative of the loss function used for supervised learning. Maximize expected reward. In contrast, reinforcement learning problems can't generally be cast as supervised learning problems. So, from this perspective, supervised learning problems are a subset of reinforcement learning problems.

But, trying to solve a supervised learning problem using a general reinforcement learning algorithm would be rather pointless; all this does is throw away structure that would have made the problem easier to solve. Various issues arise in reinforcement learning that aren't relevant to supervised learning. And, supervised learning can benefit from approaches that don't apply in the general reinforcement learning setting. So, although there are some common underlying principles and shared techniques between the fields, one doesn't typically see supervised learning discussed as a type of reinforcement learning.

References

Barto and Dietterich (2004). Reinforcement learning and its relationship to supervised learning.

share|cite|improve this answer

answered 1 hour ago

user20160

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up vote
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up vote
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down vote

It's true that any supervised learning problem can be cast as an equivalent reinforcement learning problem: Let states correspond to the input data. Let actions correspond to predictions of the output. Define reward as the negative of the loss function used for supervised learning. Maximize expected reward. In contrast, reinforcement learning problems can't generally be cast as supervised learning problems. So, from this perspective, supervised learning problems are a subset of reinforcement learning problems.

But, trying to solve a supervised learning problem using a general reinforcement learning algorithm would be rather pointless; all this does is throw away structure that would have made the problem easier to solve. Various issues arise in reinforcement learning that aren't relevant to supervised learning. And, supervised learning can benefit from approaches that don't apply in the general reinforcement learning setting. So, although there are some common underlying principles and shared techniques between the fields, one doesn't typically see supervised learning discussed as a type of reinforcement learning.

References

Barto and Dietterich (2004). Reinforcement learning and its relationship to supervised learning.

share|cite|improve this answer

answered 1 hour ago

user20160

14.1k12250

It's true that any supervised learning problem can be cast as an equivalent reinforcement learning problem: Let states correspond to the input data. Let actions correspond to predictions of the output. Define reward as the negative of the loss function used for supervised learning. Maximize expected reward. In contrast, reinforcement learning problems can't generally be cast as supervised learning problems. So, from this perspective, supervised learning problems are a subset of reinforcement learning problems.

But, trying to solve a supervised learning problem using a general reinforcement learning algorithm would be rather pointless; all this does is throw away structure that would have made the problem easier to solve. Various issues arise in reinforcement learning that aren't relevant to supervised learning. And, supervised learning can benefit from approaches that don't apply in the general reinforcement learning setting. So, although there are some common underlying principles and shared techniques between the fields, one doesn't typically see supervised learning discussed as a type of reinforcement learning.

References

Barto and Dietterich (2004). Reinforcement learning and its relationship to supervised learning.

share|cite|improve this answer

answered 1 hour ago

user20160

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answered 1 hour ago

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answered 1 hour ago

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