Investigating Reinforcement Learning Agents for Continuous State Space Environments

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Aug 28, 2017
David Von Dollen
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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.

* Withdrawn per request of Georgia Tech. The reason this this withdrawal, is that the research was done using GATech class materials provided, and may be in violation with GATech academic policy  
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