CS 4/5789: Introduction to Reinforcement Learning

Lecture 20: Upper Confidence Bound

Prof. Sarah Dean

MW 2:45-4pm
255 Olin Hall

Reminders

• Homework
  • 5789 Paper Assignments
  • PSet 7 due Monday
  • Final PA released
• Final exam is Tuesday 5/14 at 2pm

Agenda

1. Recap: Multi-Armed Bandits

2. Explore-then-Commit

3. UCB Algorithm

4. UCB Analysis

Multi-Armed Bandit

A simplified setting for studying exploration

MAB Setting

• Simplified RL setting with no state and no transitions
• \(\mathcal A=\{1,\dots,K\}\) \(K\) discrete actions ("arms")
• Stochastic rewards \(r_t\sim r(a_t)\) with expectation \(\mathbb E[r(a)] = \mu_a\)
• Finite time horizon \(T\in\mathbb Z_+\)

Multi-Armed Bandits

• for \(t=1,2,...,T\)
  • take action \(a_t\in\{1,\dots, K\}\)
  • receive reward \(r_t\)
    • \(\mathbb E[r_t] = \mu_{a_t}\)

Optimal Action and Regret

• Goal: maximize cumulative reward $$  \mathbb E\left[\sum_{t=1}^T r(a_t)  \right] = \sum_{t=1}^T \mu_{a_t}$$
• Optimal action \(a_\star = \arg\max_{a\in\mathcal A} \mu_a\)
• Definition: The regret of an algorithm which chooses actions \(a_1,\dots,a_T\) is $$R(T) = \mathbb E\left[\sum_{t=1}^T r(a^*)-r(a_t)  \right] = \sum_{t=1}^T \mu^* - \mu_{a_t} $$
• Good algorithms have sublinear regret \(R(T)\lesssim T^p\) for \(p<1\)
• Notation \(f(T)\lesssim g(T)\) means \(f(T)\leq c\cdot g(T)\) for constant \(c\)

Agenda

1. Multi-Armed Bandits

2. Explore-then-Commit

3. UCB Algorithm

4. UCB Analysis

Explore-then-Commit

• First attempt: a simple algorithm that balances exploration and exploitation into two phases

Regret of ETC

• How to set \(N\)?
• The regret decomposes $$R(T) =\sum_{t=1}^T \mu_\star - \mu_{a_t} = \underbrace{\sum_{t=1}^{NK} \mu_\star - \mu_{a_t}}_{R_1} + \underbrace{\sum_{t=NK+1}^T \mu^\star - \mu_{\hat a_\star}}_{R_2}$$
• PollEV
• Assuming that rewards are bounded, \(r_t\in[0,1]\) $$R_1+R_2 \leq NK + T (\mu^\star - \mu_{\hat a_\star})$$

Confidence intervals

\( \mu_{a} \in\left[ \hat \mu_{a} \pm c\sqrt{\frac{\log(K/\delta)}{N}}\right]\)

• We derived confidence intervals using Hoeffding's bound (\(c\) is a constant)

Regret of ETC

• How to set \(N\)?
• \(R(T) =R_1+R_2 \leq  NK + T (\mu^\star - \mu_{\hat a_\star})\)
  • \(\lesssim  NK + T \sqrt{\frac{\log(K/\delta)}{N}}\) with probability \(1-\delta\)
• Minimizing with respect to \(N\)
  • set derivative to zero: \(K - T c\sqrt{\frac{\log(K/\delta)}{4N^3}}=0\)
• Regret minimizing choice \(N=\left (\frac{cT}{2K}\sqrt{\log K/\delta}\right)^{2/3}\)
• Results in \(R(T)\lesssim T^{2/3} K^{1/3} \log^{1/3}(K/\delta)\)

Explore-then-Commit

• Theorem: For \(N\propto  ((T/K)\sqrt{\log K/\delta})^{2/3}\), the regret of ETC is bounded with probability \(1-\delta\): $$R(T)\lesssim T^{2/3} K^{1/3} \log^{1/3}(K/\delta)$$

Explore-then-Commit (Interactive Demo)

• for \(t=1,2,...,N\cdot K\)
  • \(a_t=t\mod k\), store \(r_t\)       # try each \(N\) times
• \(\hat \mu_a = \frac{1}{N} \sum_{i=1}^N r_{K\cdot i+a}\)                   # average reward
• for \(t=K+1,\dots,T\)
  • \(a_t=\arg\max_{a\in[K]} \hat \mu_a  = \hat a_\star\) # commit to best

Agenda

1. Multi-Armed Bandits

2. Explore-then-Commit

3. UCB Algorithm

4. UCB Analysis

Upper Confidence Bound

• An algorithm that adapts to confidence intervals
• Idea: Pull the arm with the highest upper confidence bound
• Principle of optimism in the face of uncertainty

Upper Confidence Bound

UCB

• Initialize \(\hat \mu_{a,1}\) and \(N_{a,1}\) for \(a\in[K]\)
• for \(t=1,2,...,T\)
  • \(a_t=\arg\max_{a\in[K]} \hat \mu_{a,t} + \sqrt{\frac{\log(KT/\delta)}{N_{a,t}}}\) # largest UCB
  • update \(N_{a_t,t+1}\) and \(\hat\mu_{a_t,t+1}\)

• number of pulls per arm:
  \(N_{a,t} = \sum_{k=1}^{t-1} \mathbf 1\{a_k=a\}\)
• average reward per arm:
  \(\hat \mu_{a,t} = \frac{1}{N_{a,t}} \sum_{k=1}^{t-1} \mathbf 1\{a_k=a\} r_k\)
• upper confidence bound:
  \(u_{a,t} =\hat \mu_{a,t} + \sqrt{\frac{\log(KT/\delta)}{N_{a,t}}}\)

UCB Intuition

• Why does it work?
• Principle of optimism in the face of uncertainty
• Two reasons to pull an arm:
  1. large confidence interval (explore)
  2. a good arm (exploit)
• Two outcomes from acting optimistically:
  1. we were correct \(\rightarrow\) high reward
  2. we were wrong \(\rightarrow\) adjust estimates

Interactive Coding Demo

Agenda

1. Multi-Armed Bandits

2. Explore-then-Commit

3. UCB Algorithm

4. UCB Analysis

Sub-optimality at \(t\)

Claim: sub-optimality at \(t\) is bounded by the width of \(a_t\)'s confidence interval

Sublinear Regret

• Regret is cumulative sub-optimality
• \(R(T) = \sum_{t=1}^T \mu_\star - \mu_{a_t} \)
  • \(\leq \sum_{t=1}^T 2\sqrt{\frac{\log(KT/\delta)}{N_{a,t}}} \)
  • \(\leq 2\sqrt {\log(KT/\delta)} \sum_{t=1}^T \sqrt{1/{N_{a,t}}} \)
• Claim: Since we only pull one arm per round, $$\sum_{t=1}^T \sqrt{1/{N_{a_t,t}}} \leq 2K\sqrt{T}$$
• Putting it all together, $$R(T) \lesssim K\sqrt {T \log(KT/\delta) }$$

Explore-then-Commit

1. Pull each arm \(N\) times and compute empirical mean \(\widehat \mu_a\)
2. For \(t=NK+1,...,T\):
       Pull \(\widehat a^* = \arg\max_a \widehat \mu_a\)

Upper Confidence Bound

For \(t=1,...,T\):

• Pull \( a_t = \arg\max_a \widehat \mu_{a,t} + \sqrt{C/N_{a,t}}\)
• Update empirical means \(\widehat \mu_{a,t}\) and counts \(N_{a,t}\)

Comparison

Preview: Contextual Bandits

Example: online advertising

Preview: Contextual Bandits

Example: online shopping

Preview: Contextual Bandits

Example: social media feeds

Preview: Contextual Bandits

• The best action will depend on the context
  • e.g. major, browsing history, demographics
• Thus we need a policy for mapping context to action

Recap

• PSet, PA released

• Explore-then-Commit
• Upper Confidence Bound

• Next lecture: Policies & contextual bandits