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.

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. In 34 tries only it completed a whole level.

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.

In this paper, an interference-aware path planning scheme for a network of cellular-connected unmanned aerial vehicles (UAVs) is proposed. In particular, each UAV aims at achieving a tradeoff between maximizing energy efficiency and minimizing both wireless latency and the interference level caused on the ground network along its path. The problem is cast as a dynamic game among UAVs. To solve this game, a deep reinforcement learning algorithm, based on echo state network (ESN) cells, is proposed. The introduced deep ESN architecture is trained to allow each UAV to map each observation of the network state to an action, with the goal of minimizing a sequence of time-dependent utility functions. Each UAV uses ESN to learn its optimal path, transmission power level, and cell association vector at different locations along its path. The proposed algorithm is shown to reach a subgame perfect Nash equilibrium (SPNE) upon convergence. Moreover, an upper and lower bound for the altitude of the UAVs is derived thus reducing the computational complexity of the proposed algorithm. Simulation results show that the proposed scheme achieves better wireless latency per UAV and rate per ground user (UE) while requiring a number of steps that is comparable to a heuristic baseline that considers moving via the shortest distance towards the corresponding destinations. The results also show that the optimal altitude of the UAVs varies based on the ground network density and the UE data rate requirements and plays a vital role in minimizing the interference level on the ground UEs as well as the wireless transmission delay of the UAV.

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.

Direct policy gradient methods for reinforcement learning and continuous control problems are a popular approach for a variety of reasons: 1) they are easy to implement without explicit knowledge of the underlying model 2) they are an "end-to-end" approach, directly optimizing the performance metric of interest 3) they inherently allow for richly parameterized policies. A notable drawback is that even in the most basic continuous control problem (that of linear quadratic regulators), these methods must solve a non-convex optimization problem, where little is understood about their efficiency from both computational and statistical perspectives. In contrast, system identification and model based planning in optimal control theory have a much more solid theoretical footing, where much is known with regards to their computational and statistical properties. This work bridges this gap showing that (model free) policy gradient methods globally converge to the optimal solution and are efficient (polynomially so in relevant problem dependent quantities) with regards to their sample and computational complexities.