TD Learning: Temporal Difference Learning

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Related lectures (30)

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Continuous Reinforcement Learning: Advanced Machine Learning

Explores continuous-state reinforcement learning challenges, value function estimation, policy gradients, and Policy learning by Weighted Exploration.

Deep Q-Learning: DeepRL1.1

Covers Deep Q-learning in deep neural networks, its application in games, backpropagation, Q-values, and V-values.

Reinforcement Learning: Basics and Applications

Covers the basics of reinforcement learning, including trial-and-error learning, Q-learning, deep RL, and applications in gaming and planning.

Relation of SARSA and Bellman equation

Explores the relation between fluctuating Q-values in SARSA and the Bellman equation through expectations and policy constancy.

Risk Minimization from Adaptively Collected Data

Explores risk minimization from adaptively collected data with guarantees for policy learning and the importance of exploration strategies.

Acquiring Data for Learning

Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.

Policy Iteration and Linear Programming in MDPs

Discusses policy iteration and linear programming methods for solving Markov Decision Processes.

Evolution of Migration Policy: Analysis and Evaluation

Covers assignments for tracing migration policy evolution and evaluating its impact.

Policy Gradient Methods: Single Neuron Example

Covers policy gradient methods using a single neuron with binary output.

Dynamic Programming: Optimal Control

Explores Dynamic Programming for optimal control, focusing on stability, stationary policy, and recursive solutions.