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Learn Reinforcement Learning from Top Universities
Reinforcement learning (RL) is a rapidly growing field that is revolutionizing the way machines learn to make decisions. From playing games like Go and chess to controlling robots and self-driving cars, RL is being used in a wide variety of applications.
If you’re interested in learning about RL and how it can be applied to real-world problems, you’re in luck! Many of the world’s top universities now offer courses and programs dedicated to RL. In this article, we’ll provide an overview of some of the best RL programs available, including what you can expect to learn and how you can get started. Whether you’re a student, a researcher, or a professional looking to upskill, there’s never been a better time to learn about RL and all the exciting possibilities it holds.
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1. Introduction to reinforcement learning — UCL & Deep Mind
Reinforcement Learning (RL) is a subfield of Machine Learning concerned with decision-making and motor control. It studies how an agent ought to take actions in an environment so as to maximize some notion of cumulative reward. This course provides an introduction to the RL framework and covers key algorithms, including value and policy iteration, Q-learning, and SARSA, as well as more recent developments such as deep RL and actor-critic methods. The course will also explore the relationship between RL and other fields, such as supervised learning, control theory, and evolutionary computation. The course will be taught by researchers from UCL and DeepMind, who are experts in the field and actively involved in cutting-edge research.
Estimated Duration: 20 hours
Instructor: David Silver
2. CS234: Reinforcement Learning — Stanford University
To realize the dreams and impact of AI requires autonomous systems that learn to make good decisions. Reinforcement learning is one powerful paradigm for doing so, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling, and healthcare. This class will provide a solid introduction to the field of reinforcement learning, and students will learn about the core challenges and approaches, including generalization and exploration.
Through a combination of lectures and written and coding assignments, students will become well-versed in key ideas and techniques for RL. Assignments will include the basics of reinforcement learning as well as deep reinforcement learning — an extremely promising new area that combines deep learning techniques with reinforcement learning.
- Define the key features of reinforcement learning that distinguish it from AI and non-interactive machine learning (as assessed by the exam).
- Given an application problem (e.g., from computer vision, robotics, etc.), decide if it should be formulated as an RL problem; if yes, be able to define it formally (in terms of the state space, action space, dynamics, and reward model), state what algorithm (from class) is best suited for addressing it and justify your answer (as assessed by the exam).
- Implement in code common RL algorithms (as assessed by the assignments).
- Describe (list and define) multiple criteria for analyzing RL algorithms and evaluate algorithms on these metrics: e.g., regret, sample complexity, computational complexity, empirical performance, convergence, etc. (as assessed by assignments and the exam).
- Describe the exploration vs. exploitation challenge and compare and contrast at least two approaches for addressing this challenge (in terms of performance, scalability, the complexity of implementation, and theoretical guarantees) (as assessed by an assignment and the exam).
Estimated Duration: 30 hours
Instructor: Emma Brunskill
3. CS885: Reinforcement Learning — University of Waterloo
The course introduces students to the design of algorithms that enable machines to learn based on reinforcements. In contrast to supervised learning, where machines learn from examples that include the correct decision, and unsupervised learning, where machines discover patterns in the data, reinforcement learning allows machines to learn from partial, implicit and delayed feedback. This is particularly useful in sequential decision-making tasks where a machine repeatedly interacts with the environment or users. Applications of reinforcement learning include robotic control, autonomous vehicles, game playing, conversational agents, assistive technologies, computational finance, operations research, etc.
- Markov decision processes
- Model-free reinforcement learning
- Model-based reinforcement learning
- Partially observable reinforcement learning
- Deep reinforcement learning
- Hierarchical reinforcement learning
- Imitation learning
- Inverse reinforcement learning
Estimated Duration: 80 hours
4. CS 285 — Deep Reinforcement Learning- UC Berkeley
Deep Reinforcement Learning (DRL) is a subfield of machine learning that combines reinforcement learning (RL) with deep learning. RL is an approach to training agents to make decisions by maximizing a cumulative reward signal. Deep learning is a set of techniques for training neural networks to learn from large amounts of data. DRL combines these two fields to allow agents to learn from high-dimensional sensory inputs, such as images and audio, and make complex decisions. Applications of DRL include robotics, gaming, and decision-making in autonomous systems.
- Introduction to Reinforcement Learning
- Policy Gradients
- Actor-Critic Algorithms
- Value Function Methods
- Deep RL with Q-Functions
- Advanced Policy Gradients
- Optimal Control and Planning
- Model-Based Reinforcement Learning
- Offline Reinforcement Learning
- Reinforcement Learning Theory Basics
- Variational Inference and Generative Models
- The connection between Inference and Control
- Inverse Reinforcement Learning
- Meta-Learning and Transfer Learning
Estimated Duration: 30 hours
Instructor: Sergey Levine
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