EL2805 Reinforcement Learning

This is a repo for the course assignments of EL2805 Reinforcement Learning at KTH 2020. The code in this repo is mainly done in Python, GoogleColab and Jupyter Notebook.

Table of contents

• Lab01
  • Problem01
  • Problem01
• Lab02

Lab01

Problem01

The Maze and the Random Minotaur

Problem02

Robbing Banks with value and policy iteration

Lab02

Lunar Lander with Dueling Deep Q-Learning and Experience Replay

Introduction

This project implements the LunarLander-v2 from OpenAI's Gym with Pytorch.
The goal is to manoeuvre the space ship so that it lands between the two flags. The landing pad is always at coordinates (0, 0). The coordinates are the first two numbers in the state vector. Reward for moving from the top of the screen to the landing pad and zero speed is about 100 ∼ 140 points. If the lander moves away from the landing pad it loses reward. The episode finishes if the lander crashes or comes to rest, receiving an additional −100 or +100 points. Each leg with ground contact is +10 points. Firing the main engine is −0.3 points each frame. Firing the side engine is −0.03 points each frame.

Results

Contact

Chieh-Ju Wu (Jeremy) - jeremy.cjwukth@gmail.com