import{s as Ae,n as Ee,o as je}from"../chunks/scheduler.37c15a92.js";import{S as Se,i as Be,g as r,s as a,r as z,A as Ie,h as l,f as n,c as o,j as qe,u as A,x as s,k as ze,y as Ge,a as i,v as E,d as j,t as S,w as B}from"../chunks/index.7cb9c9b8.js";import{H as D,E as De}from"../chunks/index.e79b7fff.js";function Fe(he){let m,F,I,N,p,O,d,ce=`In this advanced topic, we address the question: <strong>how should we monitor and keep track of powerful reinforcement learning agents that we are training in the real world and
interfacing with humans?</strong>`,U,u,ge="As machine learning systems have increasingly impacted modern life, the <strong>call for the documentation of these systems has grown</strong>.",W,f,we=`Such documentation can cover aspects such as the training data used — where it is stored, when it was collected, who was involved, etc.
— or the model optimization framework — the architecture, evaluation metrics, relevant papers, etc. — and more.`,J,h,ve=`Today, model cards and datasheets are becoming increasingly available. For example, on the Hub
(see documentation <a href="https://huggingface.co/docs/hub/model-cards" rel="nofollow">here</a>).`,K,c,be='If you click on a <a href="https://huggingface.co/models" rel="nofollow">popular model on the Hub</a>, you can learn about its creation process.',Q,g,$e=`These model and data specific logs are designed to be completed when the model or dataset are created, leaving them to go un-updated when these models are built into evolving systems in the future.
​`,V,w,X,v,xe=`Reinforcement learning systems are fundamentally designed to optimize based on measurements of reward and time.
While the notion of a reward function can be mapped nicely to many well-understood fields of supervised learning (via a loss function),
understanding of how machine learning systems evolve over time is limited.`,Y,b,ye=`To that end, the authors introduce <a href="https://www.notion.so/Brief-introduction-to-RL-documentation-b8cbda5a6f5242338e0756e6bef72af4" rel="nofollow"><em>Reward Reports for Reinforcement Learning</em></a> (the pithy naming is designed to mirror the popular papers <em>Model Cards for Model Reporting</em> and <em>Datasheets for Datasets</em>).
The goal is to propose a type of documentation focused on the <strong>human factors of reward</strong> and <strong>time-varying feedback systems</strong>.`,Z,$,Ce='Building on the documentation frameworks for <a href="https://arxiv.org/abs/1810.03993" rel="nofollow">model cards</a> and <a href="https://arxiv.org/abs/1803.09010" rel="nofollow">datasheets</a> proposed by Mitchell et al. and Gebru et al., we argue the need for Reward Reports for AI systems.',ee,x,Te="<strong>Reward Reports</strong> are living documents for proposed RL deployments that demarcate design choices.",te,y,Le=`However, many questions remain about the applicability of this framework to different RL applications, roadblocks to system interpretability,
and the resonances between deployed supervised machine learning systems and the sequential decision-making utilized in RL.`,ne,C,Re=`At a minimum, Reward Reports are an opportunity for RL practitioners to deliberate on these questions and begin the work of deciding how to resolve them in practice.
​`,ie,T,ae,L,_e=`The core piece specific to documentation designed for RL and feedback-driven ML systems is a <em>change-log</em>. The change-log updates information
from the designer (changed training parameters, data, etc.) along with noticed changes from the user (harmful behavior, unexpected responses, etc.).`,oe,R,Me="The change log is accompanied by update triggers that encourage monitoring these effects.",re,_,le,M,He=`Some of the most impactful RL-driven systems are multi-stakeholder in nature and behind the closed doors of private corporations.
These corporations are largely without regulation, so the burden of documentation falls on the public.`,se,H,Pe=`If you are interested in contributing, we are building Reward Reports for popular machine learning systems on a public
record on <a href="https://github.com/RewardReports/reward-reports" rel="nofollow">GitHub</a>.
​
For further reading, you can visit the Reward Reports <a href="https://arxiv.org/abs/2204.10817" rel="nofollow">paper</a>
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