Reinforcement Learning is a type of machine learning that allows machines and software agents to act smart and automatically detect the ideal behavior within a specific environment, in order to maximize its performance and productivity. Reinforcement Learning is becoming popular because it not only serves as an way to study how machine and software agents learn to act, it is also been used as a tool for constructing autonomous systems that improve themselves with experience. This video will give you a brief introduction to Reinforcement Learning; it will help you navigate the "Grid world" to calculate likely successful outcomes using the popular MDPToolbox package. This video will show you how the Stimulus - Action - Reward algorithm works in Reinforcement Learning. By the end of this video you will have a basic understanding of the concept of reinforcement learning, you will have compiled your first Reinforcement Learning program, and will have mastered programming the environment for Reinforcement Learning.

In this paper, we present a new approach to Transfer Learning (TL) in Reinforcement Learning (RL) for cross-domain tasks. Many of the available techniques approach the transfer architecture as a method of speeding up the target task learning. We propose to adapt and reuse the mapped source task optimal-policy directly in related domains. We show the optimal policy from a related source task can be near optimal in target domain provided an adaptive policy accounts for the model error between target and source. The main benefit of this policy augmentation is generalizing policies across multiple related domains without having to re-learn the new tasks. Our results show that this architecture leads to better sample efficiency in the transfer, reducing sample complexity of target task learning to target apprentice learning.

We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through both speech and text. The system consists of an ensemble of natural language generation and retrieval models, including neural network and template-based models. By applying reinforcement learning to crowdsourced data and real-world user interactions, the system has been trained to select an appropriate response from the models in its ensemble. The system has been evaluated through A/B testing with real-world users, where it performed significantly better than other systems. The results highlight the potential of coupling ensemble systems with deep reinforcement learning as a fruitful path for developing real-world, open-domain conversational agents.

Nils J. Nilsson of Stanford put these notes together in the mid 1990s. Before you turn up your nopse at the thought of learning from something from the 90s, remember that foundation is foundation, regardless of when it was written about. Sure, many important advancements have been made in machine learning since this was put together, as Nilsson himself says, but these notes cover much of what is still considered relevant elementary material in a straightforward and focused manner. There are no diversions related to advancements of the past few decades, which authors often want to cover tangentially even in introductory texts. There is, however, a lot of information about statistical learning, learning theory, classification, and a variety of algorithms to whet your appetite. At 200 pages, this can be read rather quickly.

Deep learning and deep reinforcement learning have recently been successfully applied in a wide range of real-world problems. Here are 15 online courses and tutorials in deep learning and deep reinforcement learning, and applications in natural language processing (NLP), computer vision, and control systems. The courses cover the fundamentals of neural networks, convolutional neural networks, recurrent networks and variants, difficulties in training deep networks, unsupervised learning of representations, deep belief networks, deep Boltzmann machines, deep Q-learning, value function estimation and optimization, and Monte Carlo tree search. Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville is a great open access textbook used by many of the courses, and Daivd Silver provides a good series of 10 video lectures in reinfrocement learning. For machine learning reviews, here are 15 online courses and tutorials for machine learning.

Imitation Learning is a sequential task where the learner tries to mimic an expert's action in order to achieve the best performance. Several algorithms have been proposed recently for this task. In this project, we aim at proposing a wide review of these algorithms, presenting their main features and comparing them on their performance and their regret bounds.

Advantage is a term that is commonly used in numerous advanced RL algorithms, such as A3C, NAF, and the algorithms that I am going to discuss (perhaps I will write another blog post for these two algorithms). To view it in a more intuitive manner, think of it as how good an action is compared to the average action for a specific state. But why do we need advantage? I will use an example posted in this forum to illustrate the idea of advantage. Have you ever played a game called "Catch"? In the game, fruits will be dropping down from the top of the screen.

Unity Machine Learning Agents allows researchers and developers to create games and simulations using the Unity Editor which serve as environments where intelligent agents can be trained using reinforcement learning, neuroevolution, or other machine learning methods through a simple-to-use Python API. For more information, see the documentation page. For a walkthrough on how to train an agent in one of the provided example environments, start here. The Agents SDK, including example environment scenes is located in unity-environment folder. For requirements, instructions, and other information, see the contained Readme and the relevant documentation.

If you are a machine learning practitioner working on generative modeling, Bayesian deep learning, or deep reinforcement learning, normalizing flows are a handy technique to have in your algorithmic toolkit. Normalizing flows transform simple densities (like Gaussians) into rich complex distributions that can be used for generative models, RL, and variational inference. TensorFlow has a nice set of functions that make it easy to build flows and train them to suit real-world data. This tutorial comes in two parts: Part 1: Distributions and Determinants. In this post, I explain how invertible transformations of densities can be used to implement more complex densities, and how these transformations can be chained together to form a "normalizing flow". Part 2: Modern Normalizing Flows: In a follow-up post, I survey recent techniques developed by researchers to learn normalizing flows, and explain how a slew of modern generative modeling techniques -- autoregressive models, MAF, IAF, NICE, Real-NVP, Parallel-Wavenet -- are all related to each other. This series is written for an audience with a rudimentary understanding of linear algebra, probability, neural networks, and TensorFlow. Knowledge of recent advances in Deep Learning, generative models will be helpful in understanding the motivations and context underlying these techniques, but they are not necessary.

A common goal in Reinforcement Learning is to derive a good strategy given a limited batch of data. In this paper, we adopt the safe policy improvement (SPI) approach: we compute a target policy guaranteed to perform at least as well as a given baseline policy. Our SPI strategy, inspired by the knows-what-it-knows paradigms, consists in bootstrapping the target policy with the baseline policy when it does not know. We develop two computationally efficient bootstrapping algorithms, a value-based and a policy-based, both accompanied with theoretical SPI bounds. Three algorithm variants are proposed. We empirically show the literature algorithms limits on a small stochastic gridworld problem, and then demonstrate that our five algorithms not only improve the worst case scenarios, but also the mean performance.