Training an AI-Powered Doomguy Leveraging Deep Reinforcement Learning

DOI

10.2991/978-94-6463-370-2_26How to use a DOI?

Keywords

Reinforcement Learning; Doom Game; Deep Learning

Abstract

Reinforcement learning (RL) has recently gained significant attention due to the impressive successes of self-driving cars and human-like performance in games such as Go or StarCraft. However, approaching this subject can be intimidating. In this research, the author aims to explore how to train a RL agent to play various Doom scenarios. This will provide an opportunity to explore various aspects of RL, such as curriculum learning, reward shaping, and machine learning in general. The author will also address how to monitor the agent’s progress during training and how to fix any issues that arise. Monitoring is crucial to ensure that the agent is learning effectively and behavior is appropriate. If any issues are detected, they can be fixed by adjusting the training process or the reward structure. The ultimate goal of this research is to train an RL agent to play deathmatch against real human players, with the author replacing humans with in-game bots. At the end of this article, you will see a human-like agent playing against bots like a real player. The author hopes to demonstrate the potential of RL in creating intelligent and autonomous agents that can compete against humans in complex environments.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Boyi Xiao
PY  - 2024
DA  - 2024/02/14
TI  - Training an AI-Powered Doomguy Leveraging Deep Reinforcement Learning
BT  - Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)
PB  - Atlantis Press
SP  - 232
EP  - 242
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-370-2_26
DO  - 10.2991/978-94-6463-370-2_26
ID  - Xiao2024
ER  -