Investigating Reinforcement Learning Agents for Continuous State Space Environments

Investigating Reinforcement Learning Agents for Continuous State Space Environments

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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Veröffentlicht in:arXiv.org 2019-03
1. Verfasser: David Von Dollen
Format: Artikel
Sprache:eng
Schlagworte:
Machine learning
Markov analysis
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Zusammenfassung: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.
ISSN:2331-8422