Playing Doom with Deep Reinforcement Learning
Manan Kalra and J C Patni. Article: Playing Doom with Deep Reinforcement Learning. IJCA Proceedings on International Conference on Recent Trends in Science, Technology, Management and Social Development ICRTSTMSD 2018(1):14-20, August 2019. Full text available. BibTeX
@article{key:article,
author = {Manan Kalra and J. C. Patni},
title = {Article: Playing Doom with Deep Reinforcement Learning},
journal = {IJCA Proceedings on International Conference on Recent Trends in Science, Technology, Management and Social Development},
year = {2019},
volume = {ICRTSTMSD 2018},
number = {1},
pages = {14-20},
month = {August},
note = {Full text available}
}
Abstract
In this work, we present a deep learning model based on reinforcement learning that is tied to an AI agent. The agent successfully learns policies to control itself in a virtual game environment directly from high-dimensional sensory inputs. The model is a convolutional neural network, trained with a variant of the Q-learning algorithm, whose input is raw pixels and whose output is a Q-value directly associated with the best possible future action. We apply our method to a first-person shooting game - Doom. We find that it outperforms all previous approaches and also surpasses a human expert.
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