CS 234 Winter 2021: Assignment #2
Due date:
Part 1 (0-4): February 5, 2021 at 6 PM (18:00) PST
Part 2 (5-6): February 12, 2021 at 6 PM (18:00) PST
These questions require thought, but do not require long answers. Please be as concise as possible.
We encourage students to discuss in groups for ...
cs 234 winter 2021 assignment 2 due date part 1 0 4 february 5
2021 at 6 pm 1800 pst part 2 5 6 february 12
2021 at 6 pm 1800 pst these questions r
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CS 234
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CS 234 Winter 2021: Assignment #2
Due date:
Part 1 (0-4): February 5, 2021 at 6 PM (18:00) PST
Part 2 (5-6): February 12, 2021 at 6 PM (18:00) PST
These questions require thought, but do not require long answers. Please be as concise as possible.
We encourage students to discuss in groups for assignments. We ask that you abide by the university
Honor Code and that of the Computer Science department. If you have discussed the problems with
others, please include a statement saying who you discussed problems with. Failure to follow these
instructions will be reported to the Office of Community Standards. We reserve the right to run a
fraud-detection software on your code. Please refer to website, Academic Collaboration and Misconduct
section for details about collaboration policy.
Please review any additional instructions posted on the assignment page. When you are ready to
submit, please follow the instructions on the course website. Make sure you test your code using
the provided commands and do not edit outside of the marked areas.
You’ll need to download the starter code and fill the appropriate functions following the instructions
from the handout and the code’s documentation. Training DeepMind’s network on Pong takes roughly
12 hours on GPU, so please start early! (Only a completed run will recieve full credit) We will give
you access to an Azure GPU cluster. You’ll find the setup instructions on the course assignment page.
Introduction
In this assignment we will implement deep Q-learning, following DeepMind’s paper ([1] and [2]) that learns
to play Atari games from raw pixels. The purpose is to demonstrate the effectiveness of deep neural networks
as well as some of the techniques used in practice to stabilize training and achieve better performance. In
the process, you’ll become familiar with PyTorch. We will train our networks on the Pong-v0 environment
from OpenAI gym, but the code can easily be applied to any other environment.
In Pong, one player scores if the ball passes by the other player. An episode is over when one of the players
reaches 21 points. Thus, the total return of an episode is between −21 (lost every point) and +21 (won
every point). Our agent plays against a decent hard-coded AI player. Average human performance is −3
(reported in [2]). In this assignment, you will train an AI agent with super-human performance, reaching at
least +10 (hopefully more!).
1
, CS 234 Winter 2021: Assignment #2
0 Distributions induced by a policy (13 pts)
In this problem, we’ll work with an infinite-horizon MDP M = hS, A, R, T , γi and consider stochastic policies
of the form π : S → ∆(A)1 . Additionally, we’ll assume that M has a single, fixed starting state s0 ∈ S for
simplicity.
(a) (written, 3 pts) Consider a fixed stochastic policy and imagine running several rollouts of this policy
within the environment. Naturally, depending on the stochasticity of the MDP M and the policy itself,
some trajectories are more likely than others. Write down an expression for ρπ (τ ), the likelihood of
sampling a trajectory τ = (s running π in M. To put this distribution in context,
0 , a0 , s1 , a1 , . . .) by
∞
recall that V π (s0 ) = Eτ ∼ρπ γ t R(st , at ) | s0 .
P
t=0
Solution:
∞
Y
ρπ (τ ) = π(at |st )T (st+1 |st , at )
t=0
(b) (written, 5 pts) Just as ρπ captures the distribution over trajectories induced by π, we can also ex-
amine the distribution over states induced by π. In particular, define the discounted, stationary state
distribution of a policy π as
∞
X
dπ (s) = (1 − γ) γ t p(st = s),
t=0
where p(st = s) denotes the probability of being in state s at timestep t while following policy π; your
answer to the previous part should help you reason about how you might compute this value. Consider
an arbitrary function f : S × A → R. Prove the following identity:
"∞ #
X 1
γ t f (st , at ) =
Eτ ∼ρπ Es∼dπ Ea∼π(s) [f (s, a)] .
t=0
(1 − γ)
Hint: You may find it helpful to first consider how things work out for f (s, a) = 1, ∀(s, a) ∈ S × A.
Hint: What is p(st = s)?
Solution:
"∞ # ∞
X X
t
Eτ ∼ρπ γ f (st , at ) = γ t Eτ ∼ρπ [f (st , at )]
t=0 t=0
= Eτ ∼ρπ [f (s0 , a0 )] + γEτ ∼ρπ [f (s1 , a1 )] + γ 2 Eτ ∼ρπ [f (s2 , a2 )] + ...
X X X X
= π(a0 |s0 )f (s0 , a0 ) + γ π(a0 |s0 ) T (s1 |s0 , a0 ) π(a1 |s1 )f (s1 , a1 ) + ...
a0 a0 s1 a1
X X
= p(s0 = s)Ea∼π(s) [f (s, a)] + γ p(s1 = s)Ea∼π(s) [f (s, a)] + ...
s s
∞
XX
= γ t p(st = s)Ea∼π(s) [f (s, a)]
s t=0
1 X 1
dπ (s)Ea∼π(s) [f (s, a)] =
= Es∼dπ Ea∼π(s) [f (s, a)]
(1 − γ) s (1 − γ)
a finite set X , ∆(X ) refers to the set of categorical distributions with support on X or, equivalently, the ∆|X |−1
1 For
probability simplex.
Page 2 of 12
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