Assignment 1: Imitation Learning

Deep Reinforcement Learning, Decision Making, and Control Fall 2021
Assignment 1: Imitation Learning

The goal of this assignment is to experiment with imitation learning, including direct behavior cloning and
the DAgger algorithm. In lieu of a human demonstrator, demonstrations will be provided via an expert policy
that we have trained for you. Your goals will be to set up behavior cloning and DAgger, and compare their
performance on a few different continuous control tasks from the OpenAI Gym benchmark suite. Turn in your
report and code as described in Section 4.
The starter-code for this assignment can be found at
https://github.com/berkeleydeeprlcourse/homework_fall2021
You have the option of running the code either on Google Colab or on your own machine. Please refer to the
README for more information on setup.
1 Behavioral Cloning
1. The starter code provides an expert policy for each of the MuJoCo tasks in OpenAI Gym. Fill in
the blanks in the code marked with Todo to implement behavioral cloning. A command for running
behavioral cloning is given in the Readme file.
We recommend that you read the files in the following order. For some files, you will need to fill in
blanks, labeled TODO.
• scripts/run hw1.py
• infrastructure/rl trainer.py
• agents/bc agent.py (another read-only file)
• policies/MLP policy.py
• infrastructure/replay buffer.py
• infrastructure/utils.py
• infrastructure/pytorch utils.py
See the homework pdf for more details.
2. Run behavioral cloning (BC) and report results on two tasks: the Ant environment, where a behavioral
cloning agent should achieve at least 30% of the performance of the expert, and one environment of your
choosing where it does not. Here is how you can run the Ant task:
python cs285/scripts/run_hw1.py \
--expert_policy_file cs285/policies/experts/Ant.pkl \
--env_name Ant-v2 --exp_name bc_ant --n_iter 1 \
--expert_data cs285/expert_data/expert_data_Ant-v2.pkl \
--video_log_freq -1
When providing results, report the mean and standard deviation of your policy’s return over multiple
rollouts in a table, and state which task was used. When comparing one that is working versus one
that is not working, be sure to set up a fair comparison in terms of network size, amount of data, and
number of training iterations. Provide these details (and any others you feel are appropriate) in the
table caption.
Note: What “report the mean and standard deviation”means is that your eval batch size should
be greater than ep len, such that you’re collecting multiple rollouts when evaluating the performance
of your trained policy. For example, if ep len is 1000 and eval batch size is 5000, then you’ll be
collecting approximately 5 trajectories (maybe more if any of them terminate early), and the logged
Berkeley CS 285 Deep Reinforcement Learning, Decision Making, and Control Fall 2021
Eval AverageReturn and Eval StdReturn represents the mean/std of your policy over these 5 rollouts.
Make sure you include these parameters in the table caption as well.
Tip: To generate videos of the policy, remove the flag --video log freq -1 However, this is slower,
and so you probably want to keep this flag on while debugging.
3. Experiment with one set of hyperparameters that affects the performance of the behavioral cloning
agent, such as the amount of training steps, the amount of expert data provided, or something that you
come up with yourself. For one of the tasks used in the previous question, show a graph of how the BC
agent’s performance varies with the value of this hyperparameter. In the caption for the graph, state
the hyperparameter and a brief rationale for why you chose it.
2 DAgger
1. Once you’ve filled in all of the TODO commands, you should be able to run DAgger.
python cs285/scripts/run_hw1.py \
--expert_policy_file cs285/policies/experts/Ant.pkl \
--env_name Ant-v2 --exp_name dagger_ant --n_iter 10 \
--do_dagger --expert_data cs285/expert_data/expert_data_Ant-v2.pkl \
--video_log_freq -1
2. Run DAgger and report results on the two tasks you tested previously with behavioral cloning (i.e., Ant
+ another environment). Report your results in the form of a learning curve, plotting the number of
DAgger iterations vs. the policy’s mean return, with error bars to show the standard deviation. Include
the performance of the expert policy and the behavioral cloning agent on the same plot (as horizontal
lines that go across the plot). In the caption, state which task you used, and any details regarding
network architecture, amount of data, etc. (as in the previous section).
3 Turning it in
1. Submitting the PDF. Make a PDF report containing: Table 1 for a table of results from Question
1.2, Figure 1 for Question 1.3. and Figure 2 with results from question 2.2.
You do not need to write anything else in the report, just include the figures with captions as described
in each question above. See the handout at
http://rail.eecs.berkeley.edu/deeprlcourse/static/misc/viz.pdf
for notes on how to generate plots.
2. 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 run logs with experiments for both the behavioral cloning (part 2, not part 3)
exercise and the DAgger exercise. Note that you can include multiple runs per exercise if you’d like,
but you must include at least one run (of any task/environment) per exercise. These folders can
be copied directly from the cs285/data folder into this new folder. Important: Disable video
logging for the runs that you submit, otherwise the files size will be too large! You
can do this by setting the flag --video log freq -1
• The cs285 folder with all the .py files, with the same names and directory structure as the original
homework repository. Also include the commands (with clear hyperparameters) that we need in
order to run the code and produce the numbers that are in your figures/tables (e.g. run “python
run hw1 behavior cloning.py –ep len 200” to generate the numbers for Section 2 Question 2) in the
form of a README file.
Berkeley CS 285 Deep Reinforcement Learning, Decision Making, and Control Fall 2021
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 and
q2 .
submit.zip
data
q1 bc ant
events.out.tfevents.1567529456.e3a096ac8ff4
q1 dagger ant
events.out.tfevents.1567529456.e3a096ac8ff4
...
cs285
agents
bc agent.py
...
policies
...
...
README.md
...
3. Turn in your assignment on Gradescope. Upload the zip file with your code and log files to HW1 Code,
and upload the PDF of your report to HW1.