Reinforcement Learning

Grokking Deep Learning for Computer Vision teaches you the concepts and tools for building intelligent, scalable computer vision systems. Using Python, OpenCV, Keras, Tensorflow, and Amazon's MxNet, you'll discover advanced techniques for building amazing end-to-end CV projects! Use this same code to get half off GANs in Action and Grokking Deep Reinforcement Learning.

If you're deeply involved in the study of artificial intelligence or automated predictive modeling, you may have come across the term "reinforcement learning," or mapping situations to actions to maximize some type of numerical reward signal. For humans, this process occurs naturally as we grow and experiment with our surroundings and see how our actions influence our rewards. Reinforcement learning deviates greatly from the normal means by which artificial intelligences are typically programmed. As noted in the book Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto, "the most important feature distinguishing reinforcement learning from other types of learning is that it uses training information that evaluates the action taken rather than instructs by giving correct actions." In short, reinforcement learning "teaches" machines how to learn from past experience and exploit that information to maximize a reward.

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This paper provides an introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. The reader is assumed to be familiar with basic machine learning concepts.

In Dynamic Programming (DP) we have seen that in order to compute the value function on each state, we need to know the transition matrix as well as the reward system. But this is not always a realistic condition. Probably it is possible to have such thing in some board games, but in video games and real life problems like self-driving car there is no way to know these information before hand. If you recall the formula of the State-Value function from "Math Behind Reinforcement Learning" article: It is not possible to compute the V(s) because p(s',r s,a) is now unknown to us. Always keep in mind that our goal is to find the policy that maximizes the reward for an agent.

Modern Technology Solutions Inc. has opened a laboratory in Huntsville, Ala., for research and development of artificial intelligence-based technology platforms for the military sector. MTSI said Friday it looks to accomplish a holistic approach to AI application through the new lab along with the company's engineering and data analytics processes. Willie Maddox, manager of AI Lab, said the company aims to apply deep reinforcement learning to address challenges related to multiagent dynamic route planning. Alexandria, Va.-based MTSI offers engineering and technology services to government customers in the missile defense, cybersecurity, intelligence, unmanned and autonomous systems, aviation, space and homeland security areas.

During holidays I wanted to ramp up my reinforcement learning skills. Knowing absolutely nothing about the field, I did a course where I was exposed to Q-learning and its "deep" equivalent (Deep-Q Learning). That's where I got exposed to OpenAI's Gym where they have several environments for the agent to play in and learn from. The course was limited to Deep-Q learning, so as I read more on my own. I realized there are now better algorithms such as policy gradients and its variations (such as Actor-Critic method).

Wang, D., and Liu, Q. Learning to draw samples: With application to amortized MLE for generative adversarial learning.

RNA, or ribonucleic acid, is present in all living cells. It acts as a messenger, carrying instructions from DNA (deoxyribonucleic acid) that dictate how proteins in the body are synthesized. And when it doesn't work as it should, it can severely affect neurological, cardiovascular, and muscular regulatory processes, resulting in effects like tumors, insulin resistance, and motor skill impairment. That's why researchers at the University of Freiburg's Department of Computer Science developed an AI system -- LEARNA -- that can learn to design RNA molecules for study. It's described in a new paper ("Learning to Design RNA") published this week on the preprint server Arxiv.org.

This page is a collection of MIT courses and lectures on deep learning, deep reinforcement learning, autonomous vehicles, and artificial intelligence taught by Lex Fridman. New lectures will be up in January. I am teaching 3 courses this January. There will be a lecture every day at 3-4:30pm for 4 weeks (Mon, Jan 7 to Fri, Feb 1). Location is room 54-100 (directions).

In this essay, we are going to address the limitations of one of the core fields of AI. In the process, we will encounter a fun allegory, a set of methods of incorporating prior knowledge and instruction into deep learning, and a radical conclusion.[1] The first part, which you're reading right now, will set up what RL is and why it (or at least a particular version of it we shall name'pure RL' and soon define) is fundamentally flawed. It will contain some explanation that can be skipped by AI practitioners -- but be sure to stick around for the discussion of recent non pure-RL work we shall argue represents the fix to pure RL's foundational flaw. But for now, let us start with a fun allegory.