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.
