INTRO TO REINFORCEMENT LEARNING

We finally kicked off our MLT Reinforcement Learning sessions with an "Intro to RL" study group at the Tokyo Metropolitan Library. Anugraha Sinha walked us through the following: Theory Introduction to RL Important elements of an RL problem Description of Markov Decision Process (MDP) and and Markov Assumption. Importance of parametrization of State, Action, Reward … Continue reading INTRO TO REINFORCEMENT LEARNING