If you're a data scientist thinking about expanding your career options into AI you've got a forest and trees problem. There's a lot going on in deep learning and reinforcement learning but do these areas hold the best future job prospects or do we need to be looking a little further forward? To try to answer that question we'll have to get out of the weeds of current development and get a higher level perspective about where this is all headed. The roots of AI are actually in the behavioral sciences migrating eventually into biology and neurology. Since the earliest imaginings of what AI might be like those thoughts have focused on machines that could behave and make decisions like humans.

For a deep dive into the current state of AI and where we might be headed in coming years, check out our free ebook "What is Artificial Intelligence," by Mike Loukides and Ben Lorica. A robot takes a big step forward, then falls. The next time, it takes a smaller step and is able to hold its balance. The robot tries variations like this many times; eventually, it learns the right size of steps to take and walks steadily. What we see here is called reinforcement learning. It directly connects a robot's action with an outcome, without the robot having to learn a complex relationship between its action and results. The robot learns how to walk based on reward (staying on balance) and punishment (falling).

Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes, but it has not yet been successfully used for automotive applications. There has recently been a revival of interest in the topic, however, driven by the ability of deep learning algorithms to learn good representations of the environment. Motivated by Google DeepMind's successful demonstrations of learning for games from Breakout to Go, we will propose different methods for autonomous driving using deep reinforcement learning. This is of particular interest as it is difficult to pose autonomous driving as a supervised learning problem as it has a strong interaction with the environment including other vehicles, pedestrians and roadworks. As this is a relatively new area of research for autonomous driving, we will formulate two main categories of algorithms: 1) Discrete actions category, and 2) Continuous actions category. For the discrete actions category, we will deal with Deep Q-Network Algorithm (DQN) while for the continuous actions category, we will deal with Deep Deterministic Actor Critic Algorithm (DDAC). In addition to that, We will also discover the performance of these two categories on an open source car simulator for Racing called (TORCS) which stands for The Open Racing car Simulator. Our simulation results demonstrate learning of autonomous maneuvering in a scenario of complex road curvatures and simple interaction with other vehicles. Finally, we explain the effect of some restricted conditions, put on the car during the learning phase, on the convergence time for finishing its learning phase.

Learning to learn by gradient descent by gradient descent

2048 is an engaging single-player, nondeterministic video puzzle game, which, thanks to the simple rules and hard-to-master gameplay, has gained massive popularity in recent years. As 2048 can be conveniently embedded into the discrete-state Markov decision processes framework, we treat it as a testbed for evaluating existing and new methods in reinforcement learning. With the aim to develop a strong 2048 playing program, we employ temporal difference learning with systematic n-tuple networks. We show that this basic method can be significantly improved with temporal coherence learning, multi-stage function approximator with weight promotion, carousel shaping, and redundant encoding. In addition, we demonstrate how to take advantage of the characteristics of the n-tuple network, to improve the algorithmic effectiveness of the learning process by i) delaying the (decayed) update and applying lock-free optimistic parallelism to effortlessly make advantage of multiple CPU cores. This way, we were able to develop the best known 2048 playing program to date, which confirms the effectiveness of the introduced methods for discrete-state Markov decision problems.

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Animals use the neurotransmitter dopamine to encode the relationship between their responses and reward. Reinforcement learning theory (1) successfully explains the role of phasic bursts of dopamine in terms of future reward maximization. Yet, dopamine clearly plays other roles in shaping behavior that have no obvious relationship to reinforcement learning, including modulating the rate at which our subjective sense of time grows in real time. On page 1273 of this issue, Soares et al. (2) closely examine the role of dopamine in mice performing a task in which they keep track of the time between two events and make decisions about this temporal duration. The results suggest the need to reassess the leading theory of dopamine function in timing--the dopamine clock hypothesis (3). They may also help explain empirical phenomena that challenge the reinforcement learning account of dopamine function.

We study the online estimation of the optimal policy of a Markov decision process (MDP). We propose a class of Stochastic Primal-Dual (SPD) methods which exploit the inherent minimax duality of Bellman equations. The SPD methods update a few coordinates of the value and policy estimates as a new state transition is observed. These methods use small storage and has low computational complexity per iteration.

Generative adversarial learning is a popular new approach to training generative models which has been proven successful for other related problems as well. The general idea is to maintain an oracle $D$ that discriminates between the expert's data distribution and that of the generative model $G$. The generative model is trained to capture the expert's distribution by maximizing the probability of $D$ misclassifying the data it generates. Overall, the system is \emph{differentiable} end-to-end and is trained using basic backpropagation. This type of learning was successfully applied to the problem of policy imitation in a model-free setup. However, a model-free approach does not allow the system to be differentiable, which requires the use of high-variance gradient estimations. In this paper we introduce the Model based Adversarial Imitation Learning (MAIL) algorithm. A model-based approach for the problem of adversarial imitation learning. We show how to use a forward model to make the system fully differentiable, which enables us to train policies using the (stochastic) gradient of $D$. Moreover, our approach requires relatively few environment interactions, and fewer hyper-parameters to tune. We test our method on the MuJoCo physics simulator and report initial results that surpass the current state-of-the-art.

Hoping to teach AI agents the common sense they need to solve arbitrary tasks without specific training, OpenAI on Monday will introduce Universe, a collection of virtualized video games, browser interfaces, and applications that serve as a training ground for code-based decision making. Universe is open-source middleware that supports Gym, the organization's toolkit for developing and evaluating reinforcement learning (RL) algorithms. RL is used to train software perform specific actions, such as playing a videogame or making a 3D model walk, under a framework that prioritizes actions through a reward scheme. Universe aims to accelerate the education of AI agents by broadening the number of available training resources. Previously, according to OpenAI, the largest RL resource consisted of 55 Atari games, the Atari Learning Environment.