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This is a living wiki entry on the AI Alignment Forum providing a conceptual overview of IRL and linking to related technical discussions; useful as a starting point but not a deep technical treatment.

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Importance: 52/100wiki pagereference

Summary

A wiki entry defining Inverse Reinforcement Learning (IRL) as a technique where AI systems infer reward functions and agent preferences by observing behavior, rather than being given explicit rewards. IRL is positioned as a key approach to AI alignment by enabling systems to learn human values through demonstration. The entry serves as a reference hub linking to related Alignment Forum posts and discussions.

Key Points

•IRL infers underlying reward functions from observed behavior, reversing the traditional RL paradigm of optimizing given explicit rewards.
•The core alignment relevance: IRL offers a pathway to align AI with human values by learning from human demonstrations rather than hand-coded objectives.
•Once an inferred reward function is learned, the AI can make decisions consistent with the observed agent's preferences and goals.
•The wiki page aggregates related Alignment Forum posts including critiques of CHAI's agenda and model misspecification issues in IRL.
•Key limitation not fully addressed: IRL faces ambiguity since many reward functions can explain the same observed behavior.

Cited by 1 page

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Agent Foundations	Approach	59.0

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Inverse Reinforcement Learning — AI Alignment Forum

Inverse Reinforcement Learning

Edited by worse, et al. last updated 30th Dec 2024

Inverse Reinforcement Learning (IRL) is a technique in the field of machine learning where an AI system learns the preferences or objectives of an agent, typically a human, by observing their behavior. Unlike traditional Reinforcement Learning (RL), where an agent learns to optimize its actions based on given reward functions, IRL works by inferring the underlying reward function from the demonstrated behavior.

In other words, IRL aims to understand the motivations and goals of an agent by examining their actions in various situations. Once the AI system has learned the inferred reward function, it can then use this information to make decisions that align with the preferences or objectives of the observed agent.

IRL is particularly relevant in the context of AI alignment, as it provides a potential approach to align AI systems with human values. By learning from human demonstrations, AI systems can be designed to better understand and respect the preferences, intentions, and values of the humans they interact with or serve.
 

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Posts tagged Inverse Reinforcement Learning

Most Relevant 

3

20Thoughts on "Human-Compatible"

TurnTrout
6y

15

3

11Model Mis-specification and Inverse Reinforcement Learning

Owain_Evans, jsteinhardt
7y

0

1

36Our take on CHAI’s research agenda in under 1500 words

Alex Flint
5y

14

1

19Learning biases and rewards simultaneously

Rohin Shah
7y

3

1

22My take on Michael Littman on "The HCI of HAI"

Alex Flint
5y

4

1

7Is CIRL a promising agenda?
Q

Chris_Leong
4y

Q

0

1

14AXRP Episode 8 - Assistance Games with Dylan Hadfield-Menell

DanielFilan
5y

1

1

6Cooperative Inverse Reinforcement Learning vs. Irrational Human Preferences

orthonormal
10y

1

1

6Delegative Inverse Reinforcement Learning

Vanessa Kosoy
9y

13

1

7AXRP Episode 2 - Learning Human Biases with Rohin Shah

DanielFilan
5y

0

1

2Humans can be assigned any values whatsoever...

Stuart_Armstrong
8y

0

1

2CIRL Wireheading

tom4everitt
8y

4

1

1(C)IRL is not solely a learning process

Stuart_Armstrong
9y

29

1

0Inverse reinforcement learning on self, pre-ontology-change

Stuart_Armstrong
10y

2

0

26Human-AI Collaboration

Rohin Shah
6y

4

Lo

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