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8 Best Free Resources To Learn Deep Reinforcement Learning Using TensorFlow


With the success of DeepMind's AlphaGo system defeating the world Go champion, reinforcement learning has achieved significant attention among researchers and developers. Deep reinforcement learning has become one of the most significant techniques in AI that is also being used by the researchers in order to attain artificial general intelligence. Below here is a list of 10 best free resources, in no particular order to learn deep reinforcement learning using TensorFlow. About: This tutorial "Introduction to RL and Deep Q Networks" is provided by the developers at TensorFlow. The topics include an introduction to deep reinforcement learning, the Cartpole Environment, introduction to DQN agent, Q-learning, Deep Q-Learning, DQN on Cartpole in TF-Agents and more.

Artificial Intelligence: Reinforcement Learning in Python


Online Courses Udemy - Complete guide to Reinforcement Learning, with Stock Trading and Online Advertising Applications BESTSELLER Created by Lazy Programmer Team, Lazy Programmer Inc English [Auto-generated], French [Auto-generated], 4 more Students also bought Data Science: Natural Language Processing (NLP) in Python Natural Language Processing with Deep Learning in Python Deep Learning Prerequisites: Linear Regression in Python Cluster Analysis and Unsupervised Machine Learning in Python Complete Python Bootcamp: Go from zero to hero in Python3 Preview this course GET COUPON CODE Description When people talk about artificial intelligence, they usually don't mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level. Reinforcement learning has recently become popular for doing all of that and more. Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn't been until recently that we've been able to observe first hand the amazing results that are possible. In 2016 we saw Google's AlphaGo beat the world Champion in Go.

What is Reinforcement Learning and how does it function?


Reinforcement learning (RL) is a subset of machine learning (ML). It allows an agent to learn through the repercussions of actions in a specific ecosystem. It can be used to train a robot with new tricks. It is a behavioral learning model where the algorithm offers data analysis feedback, directing the user to get the best outcome. It varies from other forms of supervised learning as the sample data set does not train the machine. It learns by trial and error, instead.

Researchers suggest AI can learn common sense from animals


AI researchers developing reinforcement learning agents could learn a lot from animals. In a decades-long venture to advance machine intelligence, the AI research community has often looked to neuroscience and behavioral science for inspiration and to better understand how intelligence is formed. But this effort has focused primarily on human intelligence, specifically that of babies and children. "This is especially true in a reinforcement learning context, where, thanks to progress in deep learning, it is now possible to bring the methods of comparative cognition directly to bear," the researchers' paper reads. "Animal cognition supplies a compendium of well-understood, nonlinguistic, intelligent behavior; it suggests experimental methods for evaluation and benchmarking; and it can guide environment and task design." DeepMind introduced some of the first forms of AI to combine deep learning and reinforcement learning, like the deep Q-network (DQN) algorithm, a system that played numerous Atari games at superhuman levels.

Reinforcement learning and reasoning


Reinforcement learning has seen a lot of progress in recent years. From DeepMind success with teaching machines how to play Atari games, then AlphaGo beating world champions in Go to recent OpenAI's progress on Dota 2, a multiplayer game where players divided into two teams compete with each other. The common thread is an artificial agent operating in a virtual world, where the prize is clear (e.g. On the other hand people are experimenting with AI agents operating in real-world. Each clip of Boston Dynamics gets a lot of press, showing robots performing amazing stunts, as you can see yourself here or here.

The Journey of AI & Machine Learning


Imtiaz Adam, Twitter @Deeplearn007 Updated a few sections in Sep 2020 Artificial Intelligence (AI) is increasingly affecting the world around us. It is increasingly making an impact in retail, financial services, along with other sectors of the economy.

Dynamic Frame skip Deep Q Network Artificial Intelligence

Deep Reinforcement Learning methods have achieved state of the art performance in learning control policies for the games in the Atari 2600 domain. One of the important parameters in the Arcade Learning Environment (ALE) is the frame skip rate. It decides the granularity at which agents can control game play. A frame skip value of $k$ allows the agent to repeat a selected action $k$ number of times. The current state of the art architectures like Deep Q-Network (DQN) and Dueling Network Architectures (DuDQN) consist of a framework with a static frame skip rate, where the action output from the network is repeated for a fixed number of frames regardless of the current state. In this paper, we propose a new architecture, Dynamic Frame skip Deep Q-Network (DFDQN) which makes the frame skip rate a dynamic learnable parameter. This allows us to choose the number of times an action is to be repeated based on the current state. We show empirically that such a setting improves the performance on relatively harder games like Seaquest.

Artificial Intelligence in the Creative Industries: A Review Artificial Intelligence

This paper reviews the current state of the art in Artificial Intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically Machine Learning (ML) algorithms, is provided including Convolutional Neural Network (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement Learning (DRL). We categorise creative applications into five groups related to how AI technologies are used: i) content creation, ii) information analysis, iii) content enhancement and post production workflows, iv) information extraction and enhancement, and v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, machine learning-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of machine learning in domains with fewer constraints, where AI is the `creator', remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human centric -- where it is designed to augment, rather than replace, human creativity.

AI and Wargaming Artificial Intelligence

Recent progress in Game AI has demonstrated that given enough data from human gameplay, or experience gained via simulations, machines can rival or surpass the most skilled human players in classic games such as Go, or commercial computer games such as Starcraft. We review the current state-of-the-art through the lens of wargaming, and ask firstly what features of wargames distinguish them from the usual AI testbeds, and secondly which recent AI advances are best suited to address these wargame-specific features.

Towards Behavior-Level Explanation for Deep Reinforcement Learning Artificial Intelligence

While Deep Neural Networks (DNNs) are becoming the state-of-the-art for many tasks including reinforcement learning (RL), they are especially resistant to human scrutiny and understanding. Input attributions have been a foundational building block for DNN expalainabilty but face new challenges when applied to deep RL. We address the challenges with two novel techniques. We define a class of \emph{behaviour-level attributions} for explaining agent behaviour beyond input importance and interpret existing attribution methods on the behaviour level. We then introduce \emph{$\lambda$-alignment}, a metric for evaluating the performance of behaviour-level attributions methods in terms of whether they are indicative of the agent actions they are meant to explain. Our experiments on Atari games suggest that perturbation-based attribution methods are significantly more suitable to deep RL than alternatives from the perspective of this metric. We argue that our methods demonstrate the minimal set of considerations for adopting general DNN explanation technology to the unique aspects of reinforcement learning and hope the outlined direction can serve as a basis for future research on understanding Deep RL using attribution.