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.
Investigating Reinforcement Learning Agents for Continuous State Space Environments
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