Fall 2020 CS498DL
Assignment 5: Deep Reinforcement Learning
Due date: Wednesday, December 9th, 11:59:59PM
In this assignment, you will implement the famous Deep Q-Network (DQN) and its successor Double DQN on the game of Breakout using the OpenAI Gym. The goal of this assignment to understand how Reinforcement Learning works using deep neural networks when interacting with the pixel-level information of an environment.
Download the starting code here.
The top-level notebook (
Note, as you look in the ipython notebook, in our terminology, a single episode is a game played by the agent till it loses all its lives (in this case, your agent has 5 lives). In the paper, however, an episode refers to almost 30 minutes of training on the GPU and such training is not feasible for us.
We will provide a more thorough table of expected rewards vs. number of episodes on Piazza to help with your debugging. Your goal is to have either agent.py or agent_double.py reach an evaluation score of 10. To have a fair comparison in the report, we ask you to run both files the same amount of episodes, but only one model is required to reach the evaluation score of 10.
We recommend that you look at the following links provided.
We highly recommend that you understand the Official DQN Pytorch tutorial before starting this assignment. This will give you a great starting point to implement DQN and Double DQN as the tutorial implements a version of double DQN for cartpole! However, we expect you to follow our code instructions and implement code in our format. Uploading code that does not follow our format will be receive a zero.
This is a computationally expensive assignment. It is expected that your code should run for at least 4 hours to complete 2000 episodes. You can stop training early if you reach a mean score of 10 in the game. As mentioned, we will be providing some initial expectations of score values with respect to episodes on Piazza.
This assignment requires a GPU, so use your Google Cloud credits (colab could work for this assignment as well).
The assignment is given to you in the
If you will be working on the assignment on a local machine then you will need a python environment set up with the appropriate packages. We suggest that you use Conda to manage python package dependencies (https://conda.io/docs/user-guide/getting-started.html).
Unless you have a machine with a GPU, running this assignment on your local machine will be very slow and is not recommended.
This is your last assignment, so feel free to use up your remaining late days if you so choose!
Please refer to course policies on collaborations, late submission, and extension requests.