Certificate in Reinforcement Learning Theory and Applications
-- ViewingNowThe Certificate in Reinforcement Learning Theory and Applications is a comprehensive course that equips learners with essential skills in reinforcement learning (RL). RL is a crucial area of artificial intelligence (AI), with wide-ranging applications in various industries, including gaming, robotics, finance, and healthcare.
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โข Introduction to Reinforcement Learning: Origins, basic concepts, and key terminology. Explore the difference between reinforcement learning and other machine learning paradigms.
โข Markov Decision Processes: Understand the mathematical framework for modeling decision-making processes. Learn about states, actions, rewards, and transition probabilities.
โข Dynamic Programming: Study methods for solving MDPs using value and policy iteration. Learn about Bellman equations and optimal policies.
โข Monte Carlo Methods: Dive into model-free methods for estimating value functions. Understand first-visit and every-visit Monte Carlo methods.
โข Temporal Difference Learning: Learn about model-free methods that update estimates based on the difference between subsequent estimates. Discover the power of TD(0), SARSA, and Q-learning.
โข Function Approximation: Explore methods for approximating value functions using neural networks and other function approximators. Understand the challenges and benefits of using function approximation in RL.
โข Policy Gradient Methods: Study methods for optimizing policies directly without estimating value functions. Understand the REINFORCE algorithm and its variants.
โข Deep Reinforcement Learning: Delve into the use of deep neural networks in RL. Examine the applications and limitations of DQN, DDPG, TRPO, and PPO.
โข Exploration and Exploitation Strategies: Master techniques for managing the trade-off between exploration and exploitation, such as epsilon-greedy, Boltzmann exploration, and UCB.
โข Applications of Reinforcement Learning: Discover real-world applications of RL, such as game playing, robotics, recommendation systems, and autonomous driving.
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