@nyghtowl Resources: - An Introduction to Reinforcement Learning, Sutton & Barto 1998: http://people.inf.elte.hu/lorincz/Files/RL_2006/SuttonBook.pdf - Brief Survey of Deep Reinforcement Learning: https://arxiv.org/pdf/1708.05866.pdf - David Silver’s RL Course: http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html - Deep Reinforcement Learning: Pong from Pixels: http://karpathy.github.io/2016/05/31/rl/ - Computational Neuroscience Lab: http://neuro.cs.ut.ee/demystifying-deep-reinforcement-learning/ - Playing Atari with DRL: https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf - OpenAI Baselines: https://blog.openai.com/openai-baselines-dqn/ - Gridworld example: http://www.cs.ubc.ca/~poole/demos/mdp/vi.html - TensorFlow without a PhD RL & Pong: https://github.com/GoogleCloudPlatform/tensorflow-without-a-phd/blob/master/tensorflow-rl-pong - Kaggle Winning Solutions: https://www.kaggle.com/sudalairajkumar/winning-solutions-of-kaggle-competitions/ - BigQuery Public Datasets: https://cloud.google.com/bigquery/public-data/ 

@nyghtowl Core Libraries to Explore ● DeepMind = Lab(agent-based AI research 3D platform) & PySC2 (Blizzard Entertainment’s StarCraft II API in an RL Environment) ● OpenAI = Gym (develop RL algorithms w/ any library) & Baselines (RL algorithms) & Roboschool (robot simulation in Gym) ● Facebook Research = EFL (environments for game research) & ParlAI (framework for dialog AI research)