Re­in­force­ment Learning

TagLast edit: 27 Nov 2020 16:41 UTC by Multicore

Within the field of Machine Learning, reinforcement learning refers to the study of how an agent should choose its actions within an environment in order to maximize some kind of reward. Strongly inspired by the work developed in behavioral psychology it is essentially a trial and error approach to find the best strategy.

Related: Inverse Reinforcement Learning, Machine learning, Friendly AI, Game Theory, Prediction

Consider an agent that receives an input informing the agent of the environment’s state. Based only on that information, the agent has to make a decision regarding which action to take, from a set, which will influence the state of the environment. This action will in itself change the state of the environment, which will result in a new input, and so on, each time also presenting the agent with the reward relative to its actions in the environment. The agent’s goal is then to find the ideal strategy which will give the highest reward expectations over time, based on previous experience.

Exploration and Optimization

Knowing that randomly selecting the actions will result in poor performances, one of the biggest problems in reinforcement learning is exploring the avaliable set of responses to avoid getting stuck in sub-optimal choices and proceed to better ones.

This is the problem of exploration, which is best described in the most studied reinforcement learning problem—the k-armed bandit. In it, an agent has to decide which sequence of levers to pull in a gambling room, not having any information about the probabilities of winning in each machine besides the reward it receives each time. The problem revolves about deciding which is the optimal lever and what criteria defines the lever as such.

Parallel with an exploration implementation, it is still necessary to chose the criteria which makes a certain action optimal when compared to another. This study of this property has led to several methods, from brute forcing to taking into account temporal differences in the received reward. Despite this and the great results obtained by reinforcement methods in solving small problems, it suffers from a lack of scalability, having difficulties solving larger, close-to-human scenarios.

Further Reading & References

See Also

Draft pa­pers for REALab and De­cou­pled Ap­proval on tampering

Jonathan Uesato28 Oct 2020 16:01 UTC
46 points
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Re­in­force­ment Learn­ing in the Iter­ated Am­plifi­ca­tion Framework

William_S9 Feb 2019 0:56 UTC
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Re­in­force­ment learn­ing with im­per­cep­ti­ble rewards

Vanessa Kosoy7 Apr 2019 10:27 UTC
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Re­in­force­ment Learn­ing: A Non-Stan­dard In­tro­duc­tion (Part 1)

royf29 Jul 2012 0:13 UTC
33 points
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Re­in­force­ment, Prefer­ence and Utility

royf8 Aug 2012 6:23 UTC
13 points
5 comments3 min readLW link

Re­in­force­ment Learn­ing: A Non-Stan­dard In­tro­duc­tion (Part 2)

royf2 Aug 2012 8:17 UTC
16 points
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Ap­ply­ing re­in­force­ment learn­ing the­ory to re­duce felt tem­po­ral distance

Kaj_Sotala26 Jan 2014 9:17 UTC
19 points
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Imi­ta­tive Re­in­force­ment Learn­ing as an AGI Approach

TIMUR ZEKI VURAL21 May 2018 14:47 UTC
1 point
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Del­ega­tive Re­in­force­ment Learn­ing with a Merely Sane Advisor

Vanessa Kosoy5 Oct 2017 14:15 UTC
1 point
2 comments14 min readLW link

In­verse re­in­force­ment learn­ing on self, pre-on­tol­ogy-change

Stuart_Armstrong18 Nov 2015 13:23 UTC
0 points
0 comments1 min readLW link

Clar­ifi­ca­tion: Be­havi­ourism & Reinforcement

Zaine10 Oct 2012 5:30 UTC
13 points
30 comments2 min readLW link

Is Global Re­in­force­ment Learn­ing (RL) a Fan­tasy?

[deleted]31 Oct 2016 1:49 UTC
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50 comments12 min readLW link

Del­ega­tive In­verse Re­in­force­ment Learning

Vanessa Kosoy12 Jul 2017 12:18 UTC
15 points
0 comments16 min readLW link

Vec­tor-Valued Re­in­force­ment Learning

orthonormal1 Nov 2016 0:21 UTC
2 points
0 comments4 min readLW link

Co­op­er­a­tive In­verse Re­in­force­ment Learn­ing vs. Ir­ra­tional Hu­man Preferences

orthonormal18 Jun 2016 0:55 UTC
3 points
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The Power of Reinforcement

lukeprog21 Jun 2012 13:42 UTC
144 points
474 comments4 min readLW link

Evolu­tion as Back­stop for Re­in­force­ment Learn­ing: multi-level paradigms

gwern12 Jan 2019 17:45 UTC
17 points
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IRL 1/​8: In­verse Re­in­force­ment Learn­ing and the prob­lem of degeneracy

RAISE4 Mar 2019 13:11 UTC
20 points
2 comments1 min readLW link

Keep­ing up with deep re­in­force­ment learn­ing re­search: /​r/​reinforcementlearning

gwern16 May 2017 19:12 UTC
6 points
2 comments1 min readLW link

psy­chol­ogy and ap­pli­ca­tions of re­in­force­ment learn­ing: where do I learn more?

jsalvatier26 Jun 2011 20:56 UTC
5 points
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Re­ward/​value learn­ing for re­in­force­ment learning

Stuart_Armstrong2 Jun 2017 16:34 UTC
0 points
0 comments2 min readLW link

Model Mis-speci­fi­ca­tion and In­verse Re­in­force­ment Learning

9 Nov 2018 15:33 UTC
30 points
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“Hu­man-level con­trol through deep re­in­force­ment learn­ing”—com­puter learns 49 differ­ent games

skeptical_lurker26 Feb 2015 6:21 UTC
19 points
19 comments1 min readLW link

Mak­ing a Differ­ence Tem­pore: In­sights from ‘Re­in­force­ment Learn­ing: An In­tro­duc­tion’

TurnTrout5 Jul 2018 0:34 UTC
31 points
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Suffi­ciently Ad­vanced Lan­guage Models Can Do Re­in­force­ment Learning

Zachary Robertson2 Aug 2020 15:32 UTC
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Prob­lems in­te­grat­ing de­ci­sion the­ory and in­verse re­in­force­ment learning

agilecaveman8 May 2018 5:11 UTC
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“AIXIjs: A Soft­ware Demo for Gen­eral Re­in­force­ment Learn­ing”, As­lanides 2017

gwern29 May 2017 21:09 UTC
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Some work on con­nect­ing UDT and Re­in­force­ment Learning

IAFF-User-11117 Dec 2015 23:58 UTC
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[Question] What prob­lem would you like to see Re­in­force­ment Learn­ing ap­plied to?

Julian Schrittwieser8 Jul 2020 2:40 UTC
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[Question] What messy prob­lems do you see Deep Re­in­force­ment Learn­ing ap­pli­ca­ble to?

Riccardo Volpato5 Apr 2020 17:43 UTC
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[Question] Can co­her­ent ex­trap­o­lated vo­li­tion be es­ti­mated with In­verse Re­in­force­ment Learn­ing?

Jade Bishop15 Apr 2019 3:23 UTC
12 points
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Model­ing the ca­pa­bil­ities of ad­vanced AI sys­tems as epi­sodic re­in­force­ment learning

jessicata19 Aug 2016 2:52 UTC
4 points
0 comments5 min readLW link

FHI is ac­cept­ing ap­pli­ca­tions for in­tern­ships in the area of AI Safety and Re­in­force­ment Learning

crmflynn7 Nov 2016 16:33 UTC
9 points
0 comments1 min readLW link

Book Re­view: Re­in­force­ment Learn­ing by Sut­ton and Barto

billmei20 Oct 2020 19:40 UTC
47 points
3 comments10 min readLW link

AXRP Epi­sode 1 - Ad­ver­sar­ial Poli­cies with Adam Gleave

DanielFilan29 Dec 2020 20:41 UTC
10 points
5 comments33 min readLW link

AXRP Epi­sode 3 - Ne­go­tiable Re­in­force­ment Learn­ing with An­drew Critch

DanielFilan29 Dec 2020 20:45 UTC
26 points
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Multi-di­men­sional re­wards for AGI in­ter­pretabil­ity and control

Steven Byrnes4 Jan 2021 3:08 UTC
10 points
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Is RL in­volved in sen­sory pro­cess­ing?

Steven Byrnes18 Mar 2021 13:57 UTC
14 points
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My AGI Threat Model: Misal­igned Model-Based RL Agent

Steven Byrnes25 Mar 2021 13:45 UTC
62 points
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My take on Michael Littman on “The HCI of HAI”

alexflint2 Apr 2021 19:51 UTC
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AlphaS­tar: Im­pres­sive for RL progress, not for AGI progress

orthonormal2 Nov 2019 1:50 UTC
111 points
58 comments2 min readLW link2 nominations1 review

[Question] How is re­in­force­ment learn­ing pos­si­ble in non-sen­tient agents?

SomeoneKind5 Jan 2021 20:57 UTC
3 points
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