Reinforcement Learning Strategies DQN vs. A2C

[Deep Learning] Identification of the best reinforcement learning approach on the LF2 game @ National Taipei University of Technology

Goal:

Determine the best reinforcement learning approach for the Little Fighter 2 (LF2) game

Tasks:

• Implementation of an agent that interacts with an environment for the LF2 game
• Comparison of the strategies Deep-Q-Learning (DQN) and Advantage Actor Critic (A2C) under which the agent took his deicisions
• Analysis of results based on win/lost ratio of each agent-strategy against a bot

Outcome:

In this project I could demonstrate that the A2C approach clearly outperformed DQN in the LF2 game

Tools & Resources:

• Python, Keras, Tensorflow
• LF2 environment: https://github.com/elvisyjlin/lf2gym

Project Setup

• sh setup.sh - downloads the open source LF2 from Project F and make it trainable (see here)
• Install Python 3 - installs Python 3 and get all dependencies
• pip3 install -r requirements.txt - installs the required packages
• python app.py - lets your code run