General overview: In this homework you will learn how to implement and test neural network models for solving reinforcement learning problems. The basic tasks for the homework will require to implement some extensions to the code that you have seen in the Lab. The advanced tasks will require to train and test your learning agent on a different type of input (image pixels) or Gym environment. You can just choose one of the advanced tasks to get the maximum grade. If you are interested in improving your skills, feel free to try both advanced tasks. Given the higher computational complexity of RL, in this homework you don’t need to tune learning hyperparameters using search procedures and cross-validation; however, you are encouraged to play with model hyperparameters to find a satisfactory configuration.
Technical notes: The homework should be implemented in Python using the PyTorch framework. The student can explore additional libraries and tools to implement the models; however, please make sure you understand the code you are writing because during the exam you might receive specific questions related to your implementation. The entire source code required to run the homework must be uploaded as a compressed archive in a Moodle section dedicated to the homework. If your code will be entirely included in a single Python notebook, just upload the notebook file.
