Global Certificate in Reinforcement Learning Theory and Applications
-- ViewingNowThe Global Certificate in Reinforcement Learning Theory and Applications is a comprehensive course that equips learners with essential skills in reinforcement learning (RL). RL has gained significant industry demand due to its success in various applications, such as robotics, gaming, resource management, and navigation.
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โข Introduction to Reinforcement Learning: Fundamentals, history, and key concepts. Agents, environments, actions, and rewards.
โข Markov Decision Processes: Markov properties, transition probabilities, and state values. Bellman equations and optimal policies.
โข Dynamic Programming: Policy evaluation, policy improvement, and value iteration algorithms.
โข Monte Carlo Methods: First-visit and every-visit MC methods, exploration vs exploitation, and the greedy policy.
โข Temporal Difference Learning: TD(0), SARSA, and Q-learning algorithms. Advantages and disadvantages.
โข Function Approximation: Linear, neural network, and deep learning methods for RL. Overfitting and underfitting, generalization, and feature engineering.
โข Deep Reinforcement Learning: Deep Q-Networks (DQN), Double DQN, Dueling DQN, and Rainbow. Policy gradient methods, actor-critic methods, and asynchronous methods.
โข Reinforcement Learning Applications: Game playing, robotics, resource management, and recommendation systems. Real-world challenges and best practices.
โข Evaluation and Comparison: Performance metrics, benchmarks, and experimental design. Statistical significance and error analysis.
โข Ethics and Safety: Responsible RL, fairness, transparency, and avoiding negative consequences.
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