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.

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"?

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.

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.

Once we start delving into the concepts behind Artificial Intelligence (AI) and Machine Learning (ML), we come across copious amounts of jargon related to this field of study. Understanding this jargon and how it can have an impact on the study related to ML goes a long way in comprehending the study that has been conducted by researchers and data scientists to get AI to the state it now is. In this article, I will be providing you with a comprehensive definition of supervised, unsupervised and reinforcement learning in the broader field of Machine Learning. You must have encountered these terms while hovering over articles pertaining to the progress made in AI and the role played by ML in propelling this success forward. Understanding these concepts is a given fact, and should not be compromised at any cost.

Go has many more possible configurations than chess, so its computation is evidently difficult. But, yes, in this they have used a deep reinforcement learning approach, defines as to take many possible actions to get a reward and chooses an action through which it earns a best reward. There is another story of very old and popular game Mario, Sethbling a programmer developed a computer program who learned by itself how to play Super Mario World. That program, named MarI/O taught itself by doing different tries, example it learned from its own demise and tried to jump in every next try at each and every point it got killed previously. It followed a neural network approach to learn how to play game, this approach is same as human brain's working process.

Online learning may refer to the ones with batch size to be 1, but here I mean online reinforcement learning is the RL where the agent is updated at every timestep. Naively speaking, the concept of online reinforcement learning sounds very much like how human learns, and it's very effective for tasks like stochastic games. Since it performs an update at each timestep, the agent may be more robust under the circumstances such that the current state is relatively unfamiliar. As it was updated in the past ten or so timesteps which are close to the current timesteps, the agent is more adapted to the unfamiliar current states. Also, the weights of the agent may be considered to be conditioned on the past events in the same episode, which may alleviate the issue of LSTM and memory network, that is, they still has the limit on the extent to which they can remember the distant past events in the same episode.

Deep reinforcement learning (deep RL) is a popular and successful family of methods for teaching computers tasks ranging from playing Go and Atari games to controlling industrial robots. But it is difficult to use a single neural network and conventional RL techniques to learn many different skills at once. Existing approaches usually treat the tasks independently or attempt to transfer knowledge between a pair of tasks, but this prevents full exploration of the underlying relationships between different tasks. When humans learn new skills, we take advantage of our existing skills and build new capabilities by composing and combining simpler ones. For instance, learning multi-digit multiplication relies on knowledge of single-digit multiplication, while knowing how to properly prepare individual ingredients facilitates cooking dishes with complex recipes.

Typically, a RL setup is composed of two components, an agent and an environment. Then environment refers to the object that the agent is acting on (e.g. the game itself in the Atari game), while the agent represents the RL algorithm. The environment starts by sending a state to the agent, which then based on its knowledge to take an action in response to that state. After that, the environment send a pair of next state and reward back to the agent. The agent will update its knowledge with the reward returned by the environment to evaluate its last action.