Investigating Reinforcement Learning Agents for Continuous State Space Environments
Artificial Intelligence
2019-03-13 v3
Authors:
David Von Dollen
Abstract
Given an environment with continuous state spaces and discrete actions, we investigate using a Double Deep Q-learning Reinforcement Agent to find optimal policies using the LunarLander-v2 OpenAI gym environment.
Cite
@article{arxiv.1708.02378,
title = {Investigating Reinforcement Learning Agents for Continuous State Space Environments},
author = {David Von Dollen},
journal= {arXiv preprint arXiv:1708.02378},
year = {2019}
}
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