### Sarah Dean PRO

asst prof in CS at Cornell

Prof. Sarah Dean

MW 2:45-4pm

110 Hollister Hall

0. Announcements & Recap

1. Q Function Approximation

2. Optimization & Gradient Descent

3. Stochastic Gradient Descent

4. Derivative-Free Optimization

HW2 released next Monday

5789 Paper Review Assignment (weekly pace *suggested*)

OH cancelled today, instead Thursday 10:30-11:30am

**Learning Theory Mentorship Workshop**

Application due March 10: https://let-all.com/alt22.html

Prelim Tuesday 3/22 at 7:30-9pm in Phillips 101

Closed-book, definition/equation sheet for reference will be provided

Focus: mainly Unit 1 (known models) but many lectures in Unit 2 revisit important key concepts

Study Materials: Lecture Notes 1-15, HW0&1

Lecture on 3/21 will be a review

Meta-Algorithm for Policy Iteration in Unknown MDP

- Sample \(h_1=h\) w.p. \(\propto \gamma^h\): \((s_{h_1}, a_{h_1}) = (s_i,a_i) \sim d^\pi_{\mu_0}\)
- Sample \(h_2=h\) w.p. \(\propto \gamma^h\): \(y_i = \sum_{t=h_1}^{h_1+h_2} r_t\)

**Supervision with Rollout (MC):**

\(\mathbb{E}[y_i] = Q^\pi(s_i, a_i)\)

\(\widehat Q\) via ERM on \(\{(s_i, a_i, y_i)\}_{1}^N\)

**Rollout:**

\(s_t\)

\(a_t\sim \pi(s_t)\)

\(r_t\sim r(s_t, a_t)\)

\(s_{t+1}\sim P(s_t, a_t)\)

\(a_{t+1}\sim \pi(s_{t+1})\)

...

- \(y_t =r_t + \gamma \max_a \widehat Q(s_{t+1},a) \)

**Supervision with Bellman Exp (TD):**

If \(\widehat Q = Q^\pi\) then \(\mathbb{E}[y_t] = Q^\pi(s_t, a_t)\)

**One step:**

\(s_t\)

\(a_t\sim \pi(s_t)\)

\(r_t\sim r(s_t, a_t)\)

\(s_{t+1}\sim P(s_t, a_t)\)

\(a_{t+1}\sim \pi(s_{t+1})\)

**Supervision with Bellman Opt (TD):**

- \(y_t =r_t + \gamma \widehat Q(s_{t+1},a_{t+1}) \)

If \(\widehat Q = Q^*\) then \(\mathbb{E}[y_t] = Q^*(s_t, a_t)\)

SARSA and Q-learning are simple tabular algorithms

0. Announcements & Recap

1. Q Function Approximation

2. Optimization & Gradient Descent

3. Stochastic Gradient Descent

4. Derivative-Free Optimization

By Sarah Dean