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The Cartographer
Investigating Reinforcement Learning Agents for Continuous State Space Environments
August 08, 2017 Β· Declared Dead Β· π arXiv.org
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Authors
David Von Dollen
arXiv ID
1708.02378
Category
cs.AI: Artificial Intelligence
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
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.
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