Stanford CS234 Lecture 3

Lecture 3

Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 3

recap MDP evaluation of Dynamic Programming

Dynamic Programming

• case where we know exact model (not model free)

Initialize $V_0(s)=0$ for all s

for k = 1 until convergence

for all $s$ in $S$

$V_k^{\pi }(s)=r(s,\pi (s))+\gamma \sum _{s^{\prime }\in S}P(s^{\prime }|s,\pi (s))V_{k-1}^{\pi }(s^{\prime })$

and we iterate until it converges → $||V_k^{\pi }-V_{k-1}^{\pi }||<ϵ$

• if k is finite

  → $V_k^{\pi }(s)$ is exact value of k-horizon value of state $s$ under policy $\pi$

• if k is infinite

  → $V_k^{\pi }(s)$ is approximate value of state $s$ under policy $\pi$

  → $V_k^{\pi }(s)$ ← $E_{\pi }[r_t+\gamma V_{k-1}|s_t=s]$

  resulted states of an action would be “expectation” over the state-action.

  

  “when we know the model, we can compute immediate reward and exact estimate sum of future states. then we can substitue, instead of expanding $V_{k-1}$ as sum of rewards, we can boot strap and use current estimate $V_{k-1}$”

  *Bootstrapping : 같은 종류의 예측값을 업데이트 할 때 예측값을 이용하는 것(예측값을 사용해서 예측값을 업데이트)

---

Model Free Policy Evaluation

we do not know the reward & dynamics model

Monte Carlo(MC) Policy Evaluation

→ we think of all the possible trajectories we can get from the policy and average all the returns

• no bootstrapping

• can only be applied to episodic MDPs

  → averaging over returns from complete episodes

  → requires episode to terminate

Consider a statistic $\hat{\theta }$ that provides an estimate of $\theta$ and is a function of observed data $x$ → $\hat{\theta }=f(x)$

	Definition
Bias(편차)	⁍
Variance(분산)	⁍
MSE	⁍

First-Visit MC algorithm

Initialize $N(s)=0,G(s)=0$ where $N$ is number of times a state is visited

Loop the following

$V^{\pi }(s)$ obtained is an ‘estimate’ thus might be wrong. So how do we evaluate??

• $V^{\pi }$ estimator in first-visit MC is an unbiased estimator of $E_{\pi }[G_t|s_t=s]$
• by law of large numbers, as $N(s)$→$\text{infin}$, $V^{\pi }(s)$ converges to $E_{\pi }[G_t|s_t=s]$ “consistent”

Every-Visit MC algorithm

Initialize $N(s)=0,G(s)=0$ where $N$ is number of times a state is visited

Loop the following

Note there’s no “first” on this algorithm.

• $V^{\pi }$ in every-visit MC estimator is a biased estimator

  → multiple experience on a state gets them correlated, thus data no longer ‘iid’

• stiil a consistent estimator and often has better MSE

Incremental MC algorithm

we can slowly move running average in this algorithm

Non-stationary domains → dynamics model changes over time.

Advanced topic, but incredebly important in real world

• MC estimate Example

  

  Q1. Let $\gamma =1$. First visit MC estimate of V of each state?

  Ans . $V_{s_1}$~ $V_{s_3}=1$ , $V_{s_4}$ ~ $V_{s_7}=0$ → we dont know states 4~7 without experience

  Q2. Let $\gamma =0.9$. compare first-visit & evert-visit MC estimate of $s_2$

  Ans . first-visit : 0.81 , every-visit : 0.855

  → fisrst-visit의 경우 $s_2$→$s_2$→$s_1$의 루트를 가며 첫 경험만 계산하므로

  $G(s_2)=0+0.90+0.9^21=0.81$

  따라서 $V^{\pi }(s_2)=0.81/1=0.81$이

  → every-visit의 경우 $s_2$→$s_2$→$s_1$의 루트를 가며 두 encounter 모두 계산하므로

  $G(s_2)=G_1(s_2)+G_2(S_2)=(0+0.90+0.9^21)+(0+0.9*1)=1.71$

  따라서 $V^{\pi }(s_2)=1.71/2=0.855$ 이다.

What Monte-Carlo Evaluation is doing

“approximate averaging over all possible futures by summing up one trajectory through the tree”

→ sample a return, add up all the rewards along the way

Key Limitations

• Generally high variance estimator

  → Reducing variance can require a lot of data

  → Impractical when lacking data

• Requires episodic settings

  → Episode must terminate to use to update $V$

---

Temporal Difference(TD) Policy Evaluation

Combination of Monte-Carlo & dynamic programming method

→ takes Bootstrapping aspect from dyamic programming

→ takes Sampling from Monte-Carlo

Immediately updates estimate of $V$ after each ($s,a,r,s^{\prime }$) tuple → no need to wait episode to end

TD Learning estimation

→ we’re gonna get immediate reward plus discounted sum of future rewards

Now let us compare $V^{\pi }(s)$ of incremental every-visit MC with TD

Incremental every-visit MC	⁍
TD	⁍

instead of using $G_{i,t}$ we bootstrap with immediate reward plus expected future reward.

TD Error

$${\delta }_t=r_t+\gamma V^{\pi }(s_{t+1})-V^{\pi }(s_t)$$

TD algorithm can immediately update estimated after each ($s,a,r,s^{\prime }$) tuple

• TD algorithm Example

  

  Ans. [1 0 0 0 0 0 0], we can sample tuples from trajectory as below

  

What Temporal-Difference Evaluation is doing

$$V^{\pi }(s)=V^{\pi }(s)+\alpha ([r_t+\gamma V^{\pi }(s_{t+1})]-V^{\pi }(s))$$

for state $s_t$, we update this by using a sample of $s_{t+1}$ to approximate expected next state distribution or future distribution. And then, we bootstrap by plugginf in previous estimate of $V^{\pi }$.

---

Evaluating Policy Evaluation Algorithms

Properties

	Dynamic Programming	Monte-Carlo	Temporal-Difference
Usable without model of correct domain	X	O	O
Handles non-episodic domain	O	X	O
Handle non-Markovian domain	X	O	X
Converge to true value in limit (in Markovian domain)	O	O	O
Unbiased estimate of value	X	O	X

Bias/Variance characterisics of algorithms

Batch MC and TD calculation

we might want to spend more calculation to get better estimate... for better “sample efficiency”