Reference for Reinforcement Learning

Papers RL for game playing Newest (in recent 2 years): Heinrich, Johannes, and David Silver. “Deep Reinforcement Learning from Self-Play in Imperfect-Information Games” (2016). Finn, Chelsea, Tianhe Yu, Justin Fu, Pieter Abbeel, and Sergey Levine. “Generalizing Skills with Semi-Supervised Reinforcement Learning.” arXiv (2016) Classic: Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan … Continue reading Reference for Reinforcement Learning

Open courses recommendation

Reinforcement Learning [Berkley] CS 294: Deep Reinforcement Learning, Spring 2017 [MIT] 6.S094: Deep Learning for Self-Driving Cars, Spring 2017 [Stanford] CS231n: Convolutional Neural Networks for Visual Recognition, Winter 2016. Self-Driving [Udacity] SIRAJ RAVAL’S DEEP LEARNING – Nanodegree fundation program [Udacity] Self-Driving Car Engineer Nanodegree Others [Youtube] Bay area deep learning school. McGill Artificial Intelligence Society

TensorFlow API – Activation Function

Activation Functions The activation ops provide different types of onolinearities for use in neural networks. These include: smooth nonlinearities (sigmoid, tanh, elu, softplus, and softsign) continous but not everywhere differentiable functions (relu, relu6, and relu_x) and random regularization (dropout) All activation ops apply componentwise, and produce a tensor of the same shape as the input … Continue reading TensorFlow API – Activation Function

Machine_Learning_with_TensorFlow (6)

Reinforcement Learning All these examples can be unified under a general formulation: performing an action in a scenario can yield a reward. A more technical term for scenario is a state. And we call the collection of all possible states a state-space. Performing of an action causes the state to change. But the question is, … Continue reading Machine_Learning_with_TensorFlow (6)