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Temporal difference learning
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Related lectures (32)
Markov Decision Processes: Dynamic Programming Techniques
Discusses Markov Decision Processes and dynamic programming techniques for solving optimal policies in various scenarios.
Reinforcement Learning: One-step Horizon (Bandit Problems)
Covers Bandit Problems in Reinforcement Learning, focusing on one-step horizon games and Q-values.
Policy Iteration and Linear Programming in MDPs
Discusses policy iteration and linear programming methods for solving Markov Decision Processes.
Advanced Machine Learning: Discrete Reinforcement Learning
Introduces the basics of Reinforcement Learning, covering discrete states, actions, policies, value functions, MDPs, and optimal policies.
Reinforcement Learning: Eligibility Traces
Explores Reinforcement Learning, focusing on updating previous action values along the trajectory using the SARSA algorithm.
Reinforcement Learning: Q-Learning
Introduces Q-Learning, Deep Q-Learning, REINFORCE algorithm, and Monte-Carlo Tree Search in reinforcement learning, culminating in AlphaGo Zero.
Reinforcement Learning: Q-Learning
Covers Q-Learning in reinforcement learning, exploring action values, policies, and the societal impact of algorithms.
Policy Gradient and Actor-Critic Methods: Eligibility Traces Explained
Discusses policy gradient and actor-critic methods, focusing on eligibility traces and their application in reinforcement learning tasks.
Reinforcement Learning: Policy Gradient and Actor-Critic Methods
Provides an overview of reinforcement learning, focusing on policy gradient and actor-critic methods for deep artificial neural networks.
Reinforcement Learning: TD Learning and SARSA Variants
Discusses reinforcement learning, focusing on temporal difference learning and SARSA algorithm variations.
Markov Decision Processes: Foundations of Reinforcement Learning
Covers Markov Decision Processes, their structure, and their role in reinforcement learning.
Comparison n-step SARSA and eligibility traces
Presents a quiz comparing the n-step SARSA algorithm with SARSA using eligibility traces.
Collective Learning Dynamics: Similarity Exploitation
Delves into collective learning dynamics with similarity exploitation, covering structured learning, adaptive frameworks, modeling, simulation, and experimental results.
First steps toward deep reinforcement learning
Explores the shift to deep reinforcement learning through neural networks for direct policy learning, bypassing Q-values and V-values.
Acquiring Data for Learning: Modern Approaches and Challenges
Explores modern approaches and challenges in acquiring data for learning optimal controllers through demonstrations and data-driven methods.
Elements of Reinforcement Learning
Introduces the fundamental elements of Reinforcement Learning and demonstrates their application with the Acrobot system.
Infinite-Horizon Problems: Formulation & Complexity
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Covers infinite-horizon problems in Applied Probability and Stochastic Processes.
Interactive Lecture: Reinforcement Learning
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Explores advanced reinforcement learning topics, including policies, value functions, Bellman recursion, and on-policy TD control.
Introduction to Reinforcement Learning: Concepts and Applications
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Introduces reinforcement learning, covering its concepts, applications, and key algorithms.
Optimal Marketing Strategy
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Covers decision-making in marketing based on customer behavior for optimal strategies.
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