Introduction to Statistical Learning Theory. However, alongside their state-of-the-art performance, it is still generally unclear what is the source of their generalization ability. In this paper, we discuss these challenging issues in the context of wide neural networks at large depths where we will see that the situation simplifies considerably. We would like our policies to Generalize as they do in supervised learning, but what does it mean in the context of RL? In fully deterministic environments this might not be the case. In other words, what can we deduce from the training performance of a neural network about its test performance on fresh unseen examples. Make learning your daily ritual. This is going to be directly proportional to the number of layers and the number of neurons in each layer. >> /Type /Page endobj /Parent 1 0 R /Type /Page Subsequent papers have begun to explore ways to introduce stochasticity to the games, to discourage the agents from memorizing action sequences and instead learn more meaningful behaviors. This works somewhat like data augmentation techniques, automatically generating a larger variety of training data. << I think this a very interesting line of research, which is crucial for wide spread adoption of deep reinforcement learning in industry. The MDPs vary in size and apparent complexity, but there is some underlying principle that enables generalizing to problems of different sizes. 254–263 Google Scholar 5 0 obj I see three key possible differences: 1. Using such as setup, we can now let the agent train on a set of MDPs and reserve some other MDPs as a test set. Before talking about generalization in machine learning, it’s important to first understand what supervised learning is. /Type /Page Whereas there are some satisfactory answers to the problems of approximation and optimization, much less is known about the theory of generalization. /Published (2017) 7 0 obj /Editors (I\056 Guyon and U\056V\056 Luxburg and S\056 Bengio and H\056 Wallach and R\056 Fergus and S\056 Vishwanathan and R\056 Garnett) /Type /Page We first introduce the common categories of Overall, this paper presented a nice benchmark environment and examined common practices from supervised learning. >> /Annots [ 150 0 R 151 0 R 152 0 R 153 0 R 154 0 R 155 0 R 156 0 R ] /Contents 225 0 R In reinforcement learning, things are somewhat different. 2 Generalization and Capacity Control in Deep Learning In this section, we discuss complexity measures that have been suggested, or could be used for capacity control in neural networks. /Resources 226 0 R The goal in RL is usually described as that of learning a policy for a Markov Decision Process (MDP) that maximizes some objective function, such as the expected discounted sum of rewards. 12 0 obj They then use dimensionality reduction to visualize the embeddings of these trajectories in the different models: the numbers represent stages in the trajectory, and the colors represent visual variations of the states. (Eds.) << /Contents 398 0 R To answer, supervised learning in the domain of machine learning refers to a way for the model to learn and understand data. A fundamental goal in deep learning is the characterization of trainability and generalization of neural networks as a function of their architecture and hyperparameters. /EventType (Poster) /MediaBox [ 0 0 612 792 ] The results are quite interesting: In the RL setting, they tested their method against these baselines in several problems, including CoinRun, and achieved superior results in terms of generalization. /Count 10 But can we improve generalization even further? /Annots [ 318 0 R 319 0 R 320 0 R 321 0 R 322 0 R ] We need to define our problem in terms of complexity. /Book (Advances in Neural Information Processing Systems 30) 46:28. Take a look, I have previously written on RL for combinatorial optimization, A Simple Randomization Technique for Generalization in Deep Reinforcement Learning, I have written about it in another article, Noam Chomsky on the Future of Deep Learning, An end-to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku, Kubernetes is deprecating Docker in the upcoming release, Python Alone Won’t Get You a Data Science Job, Top 10 Python GUI Frameworks for Developers, 10 Steps To Master Python For Data Science. 10 0 obj When we split the data to train set (blue) and test set (red), we see that trying to fit the training set “too well” results in an orange curve that is obviously very different from the black curve and underperforms on the test set. /Contents 323 0 R /Resources 110 0 R An MDP is characterized by a set of states S, a set of actions A, a transition function P and a reward function R. When we discuss generalization, we can propose a different formulation, in which we wish our policy to perform well on a distribution of MDPs.