Assignment 3: Q-Learning and Actor-Critic Algorithms

 CS 285 Deep Reinforcement Learning, Decision Making, and Control
Assignment 3: Q-Learning and Actor-Critic Algorithms

1 Part 1: Q-Learning
1.1 Introduction
Part 1 of this assignment requires you to implement and evaluate Q-learning for playing Atari games. The
Q-learning algorithm was covered in lecture, and you will be provided with starter code. This assignment will
be faster to run on a GPU, though it is possible to complete on a CPU as well. Note that we use convolutional
neural network architectures in this assignment. Therefore, we recommend using the Colab option if you do
not have a GPU available to you. Please start early!
1.2 File overview
The starter code for this assignment can be found at
https://github.com/berkeleydeeprlcourse/homework_fall2021/tree/master/hw3
We will be building on the code that we have implemented in the first two assignments. All files needed to run
your code are in the hw3 folder, but there will be some blanks you will fill with your solutions from homework
1. These locations are marked with # TODO: get this from hw1 or hw2 and are found in the following files:
• infrastructure/rl trainer.py
• infrastructure/utils.py
• policies/MLP policy.py
In order to implement deep Q-learning, you will be writing new code in the following files:
• agents/dqn agent.py
• critics/dqn critic.py
• policies/argmax policy.py
There are two new package requirements (opencv-python and gym[atari]) beyond what was used in the first
two assignments; make sure to install these with pip install -r requirements.txt if you are running the
assignment locally.
1.3 Implementation
The first phase of the assignment is to implement a working version of Q-learning. The default code will run the
Ms. Pac-Man game with reasonable hyperparameter settings. Look for the # TODO markers in the files listed
above for detailed implementation instructions. You may want to look inside infrastructure/dqn utils.py
to understand how the (memory-optimized) replay buffer works, but you will not need to modify it.
Once you implement Q-learning, answering some of the questions may require changing hyperparameters,
neural network architectures, and the game, which should be done by changing the command line arguments
passed to run_hw3_dqn.py or by modifying the parameters of the Args class from within the Colab notebook.
To determine if your implementation of Q-learning is correct, you should run it with the default hyperparameters on the Ms. Pac-Man game for 1 million steps using the command below. Our reference solution gets a
return of 1500 in this timeframe. On Colab, this will take roughly 3 GPU hours. If it takes much longer than
that, there may be a bug in your implementation.
To accelerate debugging, you may also test on LunarLander-v3, which trains your agent to play Lunar Lander,
a 1979 arcade game (also made by Atari) that has been implemented in OpenAI Gym. Our reference solution
with the default hyperparameters achieves around 150 reward after 350k timesteps, but there is considerable
Berkeley CS 285 Deep Reinforcement Learning, Decision Making, and Control Fall 2021
variation between runs and without the double-Q trick the average return often decreases after reaching 150.
We recommend using LunarLander-v3 to check the correctness of your code before running longer experiments
with MsPacman-v0.
1.4 Evaluation
Once you have a working implementation of Q-learning, you should prepare a report. The report should
consist of one figure for each question below. You should turn in the report as one PDF and a zip file with
your code. If your code requires special instructions or dependencies to run, please include these in a file called
README inside the zip file.
Question 1: basic Q-learning performance (DQN). Include a learning curve plot showing the performance of your implementation on Ms. Pac-Man. The x-axis should correspond to number of time steps
(consider using scientific notation) and the y-axis should show the average per-epoch reward as well as the
best mean reward so far. These quantities are already computed and printed in the starter code. They are
also logged to the data folder, and can be visualized using Tensorboard as in previous assignments. Be sure to
label the y-axis, since we need to verify that your implementation achieves similar reward as ours. You should
not need to modify the default hyperparameters in order to obtain good performance, but if you modify any
of the parameters, list them in the caption of the figure. The final results should use the following experiment
name:
python cs285/scripts/run_hw3_dqn.py --env_name MsPacman-v0 --exp_name q1
Question 2: double Q-learning (DDQN). Use the double estimator to improve the accuracy of your
learned Q values. This amounts to using the online Q network (instead of the target Q network) to select the
best action when computing target values. Compare the performance of DDQN to vanilla DQN. Since there
is considerable variance between runs, you must run at least three random seeds for both DQN and DDQN.
You may use LunarLander-v3 for this question. The final results should use the following experiment names:
python cs285/scripts/run_hw3_dqn.py --env_name LunarLander-v3 --exp_name q2_dqn_1 --seed 1
python cs285/scripts/run_hw3_dqn.py --env_name LunarLander-v3 --exp_name q2_dqn_2 --seed 2
python cs285/scripts/run_hw3_dqn.py --env_name LunarLander-v3 --exp_name q2_dqn_3 --seed 3
python cs285/scripts/run_hw3_dqn.py --env_name LunarLander-v3 --exp_name q2_doubledqn_1 --
double_q --seed 1
python cs285/scripts/run_hw3_dqn.py --env_name LunarLander-v3 --exp_name q2_doubledqn_2 --
double_q --seed 2
python cs285/scripts/run_hw3_dqn.py --env_name LunarLander-v3 --exp_name q2_doubledqn_3 --
double_q --seed 3
Submit the run logs (in cs285/data) for all of the experiments above. In your report, make a single graph that
averages the performance across three runs for both DQN and double DQN. See scripts/read results.py
for an example of how to read the evaluation returns from Tensorboard logs.
Question 3: experimenting with hyperparameters. Now let’s analyze the sensitivity of Q-learning to
hyperparameters. Choose one hyperparameter of your choice and run at least three other settings of this
hyperparameter, in addition to the one used in Question 1, and plot all four values on the same graph. Your
choice what you experiment with, but you should explain why you chose this hyperparameter in the caption.
Examples include: (1) learning rates; (2) neural network architecture for the Q network, e.g., number of layers,
hidden layer size, etc; (3) exploration schedule or exploration rule (e.g. you may implement an alternative
to -greedy and set different values of hyperparameters), etc. Discuss the effect of this hyperparameter on
performance in the caption. You should find a hyperparameter that makes a nontrivial difference on performance. Note: you might consider performing a hyperparameter sweep for getting good results in Question 1,
in which case it’s fine to just include the results of this sweep for Question 3 as well, while plotting only the
best hyperparameter setting in Question 1. The final results should use the following experiment name:
Berkeley CS 285 Deep Reinforcement Learning, Decision Making, and Control Fall 2021
python run_hw3_dqn.py --env_name LunarLander-v3 --exp_name q3_hparam1
python run_hw3_dqn.py --env_name LunarLander-v3 --exp_name q3_hparam2
python run_hw3_dqn.py --env_name LunarLander-v3 --exp_name q3_hparam3
You can replace LunarLander-v3 with PongNoFrameskip-v4 or MsPacman-v0 if you would like to test on a
different environment.
2 Part 2: Actor-Critic
2.1 Introduction
Part 2 of this assignment requires you to modify policy gradients (from hw2) to an actor-critic formulation. Note that evaluation may take longer for actor-critic than policy gradient (on half-cheetah) due to the
significantly larger number of training steps for the value function.
Recall the policy gradient from hw2:
∇θJ(θ) ≈
1
N
X
N
i=1
X
T
t=1
∇θ log πθ(ait|sit)
 X
T
t
0=t
γ
t
0−t
r(sit0 , ait0 )
!
− V
π
φ
(sit)
!
.
In this formulation, we estimate the Q function by taking the sum of rewards to go over each trajectory, and
we subtract the value function baseline to obtain the advantage
A
π
(st, at) ≈
 X
T
t
0=t
γ
t
0−t
r(st
0 , at
0 )
!
− V
π
φ
(st)
In practice, the estimated advantage value suffers from high variance. Actor-critic addresses this issue by
using a critic network to estimate the sum of rewards to go. The most common type of critic network used is
a value function, in which case our estimated advantage becomes
A
π
(st, at) ≈ r(st, at) + γV π
φ
(st+1) − V
π
φ
(st)
In this assignment we will use the same value function network from hw2 as the basis for our critic network.
One additional consideration in actor-critic is updating the critic network itself. While we can use Monte
Carlo rollouts to estimate the sum of rewards to go for updating the value function network, in practice we
fit our value function to the following target values:
yt = r(st, at) + γV π
(st+1)
we then regress onto these target values via the following regression objective which we can optimize with
gradient descent:
min
φ
X
i,t
(V
π
φ
(sit) − yit)
2
In theory, we need to perform this minimization every time we update our policy, so that our value function
matches the behavior of the new policy. In practice however, this operation can be costly, so we may instead
just take a few gradient steps at each iteration. Also note that since our target values are based on the
old value function, we may need to recompute the targets with the updated value function, in the following
fashion:
1. Update targets with current value function
2. Regress onto targets to update value function by taking a few gradient steps
3. Redo steps 1 and 2 several times
In all, the process of fitting the value function critic is an iterative process in which we go back and forth
between computing target values and updating the value function to match the target values. Through
experimentation, you will see that this iterative process is crucial for training the critic network.
Berkeley CS 285 Deep Reinforcement Learning, Decision Making, and Control Fall 2021
2.2 Implementation
Your code will build off your solutions from homework 2. You will need to fill in the TODOS for the following
parts of the code.
• In policies/MLP_policy.py, implement the update function for the class MLPPolicyAC. You should
note that the AC policy class is in fact the same as the policy class you implemented in the policy
gradient homework (except we no longer have a nn baseline).
• In agents/ac_agent.py, finish the train function. This function should implement the necessary critic
updates, estimate the advantage, and then update the policy. Log the final losses at the end so you can
monitor it during training.
• In agents/ac_agent.py, finish the estimate_advantage function: this function uses the critic network
to estimate the advantage values. The advantage values are computed according to
A
π
(st, at) ≈ r(st, at) + γV π
φ
(st+1) − V
π
φ
(st)
Note: for terminal timesteps, you must make sure to cut off the reward to go (i.e., set it to zero), in
which case we have
A
π
(st, at) ≈ r(st, at) − V
π
φ
(st)
• critics/bootstrapped_continuous_critic.py complete the TODOS in update. In update, perform
the critic update according to process outlined in the introduction. You must perform
self.num_grad_steps_per_target_update * self.num_target_updates
number of updates, and recompute the target values every
self.num_grad_steps_per_target_update number of steps.
2.3 Evaluation
Once you have a working implementation of actor-critic, you should prepare a report. The report should
consist of figures for the question below. You should turn in the report as one PDF (same PDF as part 1) and
a zip file with your code (same zip file as part 1). If your code requires special instructions or dependencies
to run, please include these in a file called README inside the zip file.
Question 4: Sanity check with Cartpole Now that you have implemented actor-critic, check that your
solution works by running Cartpole-v0.
python run_hw3_actor_critic.py --env_name CartPole-v0 -n 100 -b 1000 --exp_name q4_ac_1_1
-ntu 1 -ngsptu 1
In the example above, we alternate between performing one target update and one gradient update step for
the critic. As you will see, this probably doesn’t work, and you need to increase both the number of target
updates and number of gradient updates. Compare the results for the following settings and report which
worked best. Do this by plotting all the runs on a single plot and writing your takeaway in the caption.
python run_hw3_actor_critic.py --env_name CartPole-v0 -n 100 -b 1000 --exp_name q4_100_1 -
ntu 100 -ngsptu 1
python run_hw3_actor_critic.py --env_name CartPole-v0 -n 100 -b 1000 --exp_name q4_1_100 -
ntu 1 -ngsptu 100
python run_hw3_actor_critic.py --env_name CartPole-v0 -n 100 -b 1000 --exp_name q4_10_10 -
ntu 10 -ngsptu 10
At the end, the best setting from above should match the policy gradient results from Cartpole in hw2 (200).
Berkeley CS 285 Deep Reinforcement Learning, Decision Making, and Control Fall 2021
Question 5: Run actor-critic with more difficult tasks Use the best setting from the previous question
to run InvertedPendulum and HalfCheetah:
python run_hw3_actor_critic.py --env_name InvertedPendulum-v2 --ep_len 1000 --discount
0.95 -n 100 -l 2 -s 64 -b 5000 -lr 0.01 --exp_name q5_<ntu>_<ngsptu> -ntu <> -ngsptu <>
where <ntu> <ngsptu> is replaced with the parameters you chose.
python run_hw3_actor_critic.py --env_name HalfCheetah-v2 --ep_len 150 --discount 0.90 --
scalar_log_freq 1 -n 150 -l 2 -s 32 -b 30000 -eb 1500 -lr 0.02 --exp_name q5_<ntu>_<
ngsptu> -ntu <> -ngsptu <>
Your results should roughly match those of policy gradient. After 150 iterations, your HalfCheetah return
should be around 150. After 100 iterations, your InvertedPendulum return should be around 1000. Your
deliverables for this section are plots with the eval returns for both enviornments.
As a debugging tip, the returns should start going up immediately. For example, after 20 iterations, your
HalfCheetah return should be above -40 and your InvertedPendulum return should near or above 100. However,
there is some variance between runs, so the 150-iteration (for HalfCheetah) and 100-iteration (for InvertedPendulum) results are the numbers we use to grade.
3 Submitting the code and experiment runs
In order to turn in your code and experiment logs, create a folder that contains the following:
• A folder named data with all the experiment runs from this assignment. Do not change the names
originally assigned to the folders, as specified by exp name in the instructions. Video logging
is disabled by default in the code, but if you turned it on for debugging, you will need to run
those again with --video log freq -1, or else the file size will be too large for submission.
• The cs285 folder with all the .py files, with the same names and directory structure as the original
homework repository (excluding the data folder). Also include any special instructions we need to run
in order to produce each of your figures or tables (e.g. “run python myassignment.py -sec2q1” to generate
the result for Section 2 Question 1) in the form of a README file.
As an example, the unzipped version of your submission should result in the following file structure. Make
sure that the submit.zip file is below 15MB and that they include the prefix q1 , q2 , q3 , etc.
Berkeley CS 285 Deep Reinforcement Learning, Decision Making, and Control Fall 2021
submit.zip
data
q1 dqn ...
events.out.tfevents.1567529456.e3a096ac8ff4
q2 ac ...
events.out.tfevents.1567529456.e3a096ac8ff4
...
cs285
agents
ac agent.py
...
policies
...
...
README.md
...
Turn in your assignment on Gradescope. Upload the zip file with your code and log files to HW3 Code, and
upload the PDF of your report to HW3.