Investigating Reinforcement Learning Agents for Continuous State Space Environments
arXiv:1708.02378
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.