Also, make sure to check out Google DeepMind's lab: https://github.com/deepmind/lab For the record: no, I am not an Edge user. WE WOULD LIKE TO THANK OUR GENEROUS PATREON SUPPORTERS WHO MAKE TWO MINUTE PAPERS POSSIBLE: Sunil Kim, Daniel John Benton, Dave Rushton-Smith. Subscribe if you would like to see more of these! Music: Dat Groove by Audionautix is licensed under a Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/)

Deep RL is where deep learning is used in conjunction with RL to simplify the reward function in cases where the search space is very large, or the environment is very complicated with multi-dimensional states, actions, and rewards. The use of deep learning with RL is also known as Q-learning in which a deep learning network is used as a function approximator (called the Q function), predicting the reward for an input, rather than trying to explore and store rewards and actions for every state. Also, in simulation environments, by simply feeding pixels of an environment through a neural network, it allows the reinforcement algorithm to better understand its environment. For the most part, RL is being used to teach AI systems how to play games, as games provide a safe and bounded environment for learning. For example, AlphaGo uses RL (in combination with other techniques) and similar techniques to have AI learn Atari games, or become champions at Poker.

It aims to promote and develop friendly AI in a way to benefit humanity as a whole. It aims to "freely collaborate" by making its patents and research open to the public.

Mini World of Bits ("MiniWoB") is a benchmark for reinforcement learning agents who interact with websites. The agents perceive the raw pixels of a small (210x160 pixel) webpage and produce keyboard and mouse actions. The environments are written in HTML/Javascript/CSS and are designed to test the agent's capacity to interact with common web browser elements, such as buttons, text fields, slides, date pickers, etc. The environments of this benchmark are accessible through the OpenAI Universe. Each environment is an HTML page that is 210 pixels high, 160px wide (i.e.