## CS 4/5789: Introduction to Reinforcement Learning

### Lecture 26

Prof. Sarah Dean

MW 2:45-4pm
110 Hollister Hall

## Agenda

0. Announcements & Recap

1. Real World RL

2. Specification and Risks

3. Does RL Work?

## Announcements

5789 Paper Review Assignment (weekly pace suggested)

HW 4 due 5/9 -- don't plan on extentions

Final exam Monday 5/16 at 7pm
Review session in lecture 5/9

Course evaluations open tomorrow

## Recap: IL vs. IRL

Supervised Learning

Policy

Dataset of expert trajectory

...

$$\pi$$(       ) =

$$(x=s, y=a^*)$$

imitation

inverse RL

Goal: understand/predict behaviors

## Recap: Max-Ent IRL

• For $$k=0,\dots,K-1$$:
1. $$\pi^k = \mathsf{SoftVI}(w_k^\top \varphi)$$
2. $$w_{k+1} = w_k + \eta (\mathbb E_{d^{\pi^*}_\mu}[\varphi (s,a)] - \mathbb E_{d^{\pi^k}_\mu}[\varphi (s,a)])$$
• Return $$\bar \pi = \mathsf{Unif}(\pi^0,\dots \pi^{K-1})$$
• Input: reward function $$r$$. Initialize $$V_H^*(s) = 0$$
• For $$h=H-1,\dots 0$$:
1. $$Q_h^*(s,a) = r(s,a) + \mathbb E_{s'\sim P}[V_{h+1}(s')]$$
2. $$\pi_h^*(a|s) \propto \exp(Q^*_h(s,a))$$
3. $$V_h^*(s) = \log\left(\sum_{a\in\mathcal A} \exp(Q^*_h(s,a) \right)$$

Soft-VI

## Agenda

0. Announcements & Recap

1. Real World RL

2. Specification and Risks

3. Does RL Work?

## Real World RL

AlphaGo vs. Lee Sedol, 2016

## Agenda

0. Announcements & Recap

1. Real World RL

2. Specification and Risks

3. Does RL Work?

## RL Specification

Markov decision process $$\mathcal M = \{\mathcal S, ~\mathcal A, ~P, ~r, ~\gamma\}$$

$$s_t$$

$$r_t$$

$$a_t$$

$$\pi$$

$$\gamma$$

$$P$$

• action space and discount known
• states and reward signals observed
• transition probabilities unknown

actions & states determine environment

discount & reward determine objective

## Specifying Horizon/Discount

Large discount factor leads to short-sighted agent

• $$0$$ cost for $$a_0$$
• $$2\epsilon$$ cost for $$a_1$$
• $$\epsilon$$ reward in $$s_0$$
• $$1$$ reward in $$s_1$$

$$V^{a_0}(s_0) = \frac{\epsilon}{1-\gamma}$$ and $$V^{a_1}(s_0) = \frac{1}{1-\gamma} - \frac{2\epsilon}{\gamma}$$

## Specifying Reward

The promise of RL:

translate specified objective into desired behavior

The reality:

## Risk: Reward Hacking

While everyone seemed focused on how many views a video got, we thought the amount of time someone spent watching a video was a better way to understand whether a viewer really enjoyed it."

## Risk: Reward Hacking

Misinformation, toxicity, and violent content are inordinately prevalent among reshares"

## Reward Design

Inverse Reward Design (NeuRIPS, 2017)

Idea: treat specified reward as imperfect proxy

Then attempt to learn true reward from other feedback

## Specifying States & Actions

The interface through which the agent sees and impacts the world

Also delimits reasoning about the world

$$s_t$$

$$a_t$$

## Risk: Too Much Information

Evolving an oscillator on hardware (Bird & Layzell, 2002)

Result: a "network of transistors sensing and utilising the radio waves emanating from nearby PCs"

## Risk: Too Little Information

The first Tesla autopilot fatality in 2016

Safety systems failed to detect white truck against bright sky

"vehicles [...] will no longer be equipped with radar. Instead, these will [...] rely on camera vision and neural net processing." (Tesla, 2021)

## Risk: Inappropriate Actuation

Learning to influence other drivers

Excessive caution around other drivers

Excessive aggression

## Agenda

0. Announcements & Recap

1. Real World RL

2. Specification and Risks

3. Does RL Work?

## Does RL Work?

1. Model-based design and optimization works better

Three strikes against RL:

ex - Model Predictive Control at Boston Dynamics

## Does RL Work?

1. Model-based design and optimization works better

Three strikes against RL:

data-driven optimization suffers from local minima, large sample complexity (Deep RL doesn't work yet, 2018)

## Does RL Work?

2. Simulation essentially necessary, but huge sim2real gap

Three strikes against RL:

RL exploits bugs in simulator code (Nathan Lambert, 2021)

## Does RL Work?

3. Questionable evaluation practices

Three strikes against RL:

State-of-the-art algorithms outperformed by simple baselines: Simple random search provides a competitive approach

## Generality?

This perspective ignores the instance-specific tuning that often goes into making RL algorithms work

"Machine learning has become alchemy" Ali Rahimi & Ben Recht, 2017

King Midas cursed by Dionysus

When Silicon Valley tries to imagine superintelligence, what it comes up with is no-holds-barred capitalism.

Ted Chiang, 2018.

I think many AV teams could handle a pogo stick user in pedestrian crosswalk. Having said that, bouncing on a pogo stick in the middle of a highway would be really dangerous. Rather than building AI to solve the pogo stick problem, we should partner with the government to ask people to be lawful and considerate. Safety isn’t just about the quality of the AI technology.

- Andrew Ng, 2018

## All ML is RL once deployed

ex - credit-score designed within supervised learning framework, but used to make lending decisions

$$\{x_i, y_i\}$$

$$x$$

$$\widehat y$$

$$(x, y)$$

When a measure becomes a target, it ceases to be a good measure"

Goodhardt's law

## ML and social dynamics

When a measure becomes a target, it ceases to be a good measure"

Goodhardt's law

## ML and social dynamics

Buzzfeed noticed the success of content that exploited racial divisions, fad/junky science, extremely disturbing news and gross images.

## ML and social dynamics

Some political parties in Europe told Facebook the algorithm had made them shift their policy positions so they resonated more on the platform, according to the documents."

Technologies are developed  and used within a particular social, economic, and political context. They arise out of a  social structure, they are grafted on to it, and they may reinforce it or destroy it, often in ways that are neither foreseen  nor foreseeable.”

Ursula Franklin, 1989

## Exo-Feedback

control feedback

data feedback

external feedback

"...social, economic, and political context..."

"...neither foreseen nor forseeable..."

## Recap

1. Real World RL

2. Specification and Risks

3. Does RL Work?

## Upcoming

1. AlphaGo case study

2. Review for final

By Sarah Dean

Private