Tutorial: Sampling And Measuring Generators From: Tutorial: Sampling And Measuring Generators In this part we’ll see how we can understand the generative space of a procedural generator by sampling it. Simple Generator Sampling and Representation Our hope

PCG Tutorial: Generative & Possibility Space From: Tutorial: Generative & Possibility Space Definition Generative Space: the set of all the things it can generate. Possibility Space: the set of all possible Minecraft worlds we can imageine, represent or describe.

2019 CIFAR DLRL Summer School Lectures CIFAR DLRL - Generative Models 2 Definition Generative models: models that output high-dimensional data (or anything involving a GAN, VAE, PixelCNN, etc). It is useful for lots of problems beyond density estimation and sampling random images. What Can We

2019 CIFAR DLRL Summer School Lectures CIFAR DLRL - Generative Models I Recap ML as a Bag of Tricks Fast special cases K-means Kernel Density Estimation SVMs Boosting Random Forests Extensible family Mixture of Gaussians Latent variable models Gaussian processes Deep neural nets Bayesian neural

2019 CIFAR DLRL Summer School Lectures CIFAR DLRL - CNN Computer Vision The structure of images is complex invariances scale translation cropping dilation homogeneity Perceptual sensitivity color edges orientations Extracting semantics is challenging occlusion deformation illumination viewpoint object pose Convolutional Network Translation invariance

2019 CIFAR DLRL Summer School Lectures CIFAR DLRL - Neural Networks I Neural Networks Making predictions with feedforward neural networks. Artificial Neuron Neuron pre-activation $a(x) = b + \Sigma_{i}\omega_{i}x_{i} = b + w^{T}x$ Neuron activation $h(x) = g(a(x)) = g(

2019 CIFAR DLRL Summer School Lectures CIFAR DLRL - Recap 0 Recap Maximum Likelihood As we acquire more data, we can safely consider more complex hypotheses. The approach that we have considered to finding parameters for far is a maximum likelihood approach. The probability

Deep RL Bootcamp Notes -- Lec2 Sampling-Based Approximation Lecture Note: Sample-based Approximations and Fitted Learning Recap Q-Value Q-Values Bellman Equation: $$Q ^\ast (s, a) = \sum _{s\prime} P(s\prime | s, a) (R(s, a, s\prime) + \gamma \max

ReinforcementLearningTutorials Deep RL Bootcamp Notes -- Lec1 Markov Decision Process Lecture Notes Intro to MDPs and Exact Solution Methods We have the initial state //(s_{t}//). The agent gets to choose an action at time //(t//), action //(a_t//) as