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Reinforcement Learning is one of the most in demand research topics whose popularity is only growing day by day. Reinforcement learning (RL) translates to learning by interacting from the surrounding environment. An RL expert learns from experience, rather than being explicitly taught, which is essentially trial and error learning. To understand RL, Analytics Insight compiles the Top 10 Reinforcement Learning Courses and Certifications in 2020.
The reinforcement learning specialization consists of four courses that explore the power of adaptive learning systems and artificial intelligence (AI). On this MOOC course, you will learn how Reinforcement Learning (RL) solutions help to solve real-world problems through trial-and-error interaction by implementing a complete RL solution.
By the end of this specialization, you will understand the foundations of modern probabilistic artificial intelligence (AI). The tools learned in this specialization can be applied to game development (AI), oil & gas pipelines, industrial control systems, customer interaction, smart assistants, recommender systems, supply chain, industrial control, finance and more.
In this course, you will find out about the foundations of Reinforcement Learning methods like value/policy iteration, q-learning, policy gradient, etc using deep neural networks. Also known as "the hype train", state of the art RL algorithms and how to apply for practical problems, and, teaching the neural network how to play games
This course introduces you to the fundamentals of Reinforcement Learning. When you finish this course, you will formalize problems as Markov Decision Processes. Understand basic exploration methods and the exploration/exploitation tradeoff. You will learn value functions, as a general-purpose tool for optimal decision-making. This course teaches you the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL. After completing this course, you will be able to start using RL for real problems.
In this course you will learn and understand Reinforcement Learning. You will learn how to manage and install software for machine, how to implement common RL algorithm, how to generate a random MDP problem, and how to solve various reinforcement learning problems.
At the end of this course, you will have a logical understanding of Reinforcement learning and know the most appropriate solutions for RL problems. This course is best suited for web developers, software developers, programmers and anyone who wants to learn reinforcement learning
Platform- Udemy
Offered By- Atamai AI Team
USP- 7 hour on-demand video, 5 articles, 3 downloadable resource, Certificate of completion
You will learn Markov Decision Process, deterministic and stochastic environments, Bellman Equation, Q Learning, exploration vs exploitation, algorithm scaling up, Neural Networks as function approximators, deep reinforcement learning, DQN, Improvements to DQN, tuning parameters and general recommendations.
This course is best suited for anyone who is interested in artificial intelligence, data science, machine learning, deep learning and reinforcement learning.
You will learn how to code policy gradient, deep deterministic policy gradients (DDPG), twin delayed deep deterministic policy gradients (TD3) and actor critic algorithms in PyTorch. Besides, this course will teach you how to implement cutting edge artificial intelligence research papers in Python
This course is designed for beginners to learn machine learning. Some of the most exciting advances in artificial intelligence have occurred by challenging neural networks to play games. The instructor will introduce the concept of reinforcement learning, by teaching you how to code a neural network in Python capable of delayed gratification.
In this course you will master deep reinforcement learning skills that are powering advances in AI. You will start applying these to applications like video games and robotics. Before taking this course, you should have taken a graduate-level machine-learning course and should have had some exposure to reinforcement learning from previous courses or a seminar in computer science.
You will learn cutting-edge deep reinforcement learning algorithms from Deep Q-Networks (DQN) to deep deterministic policy gradients (DDPG). You can apply these concepts to train agents to walk, drive, or perform other complex tasks, and build a robust portfolio of deep reinforcement learning projects. This course will introduce you to the foundations of reinforcement learning, value-based methods, evolutionary algorithms and policy-gradient methods, and additionally you will learn how to apply reinforcement learning methods to applications that involve multiple, interacting agents.
In this course, you will be introduced to the world of reinforcement learning. You will learn how to frame reinforcement learning problems and start tackling classic examples like news recommendation, learning to navigate in a grid-world, and balancing a cart-pole.
You would explore the basic algorithms from multi-armed bandits, dynamic programming, TD (temporal difference) learning, and progress towards larger state space using function approximation, in particular using deep learning. Along the way, you will get introduced to Project Malmo, a platform for Artificial Intelligence experimentation and research built on top of the Minecraft game.
