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Investigating Reinforcement Learning Agents for Continuous State Space Environments

Published 8 Aug 2017 in cs.AI | (1708.02378v3)

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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Authors (1)

  1. David Von Dollen 

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