Investigating Reinforcement Learning Agents for Continuous State Space Environments

David Von Dollen

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arXiv · 1708.02378

Investigating Reinforcement Learning Agents for Continuous State Space Environments

David Von Dollen

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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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David Von Dollen. 2017-08-08. Investigating Reinforcement Learning Agents for Continuous State Space Environments. https://arxiv.org/abs/1708.02378

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