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UCL / DeepMind RL Lecture 1: Introduction to Reinforcement Learning

1:28:12
2

UCL / DeepMind RL Lecture 2: Markov Processes, Reward Processes & Bellman Equations

1:42:04
3

UCL / DeepMind RL Lecture 3: Planning by Dynamic Programming — Policy & Value Iteration

1:39:09
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UCL / DeepMind RL Lecture 4: Model-Free Prediction — Monte Carlo, TD(0) & TD(λ)

1:37:01
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UCL / DeepMind RL Lecture 5: Model-Free Control — ε-Greedy SARSA & Off-Policy Q-Learning

1:36:30
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UCL / DeepMind RL Lecture 6: Value Function Approximation & Deep Q-Networks (DQN)

1:36:45
7

RL Course by David Silver - Lecture 7: Policy Gradient Methods

1:33:58
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RL Course by David Silver - Lecture 8: Integrating Learning and Planning

1:40:12
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Lecture 9: Exploration and Exploitation (COMP0089)

1:39:18
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Lecture 10: Classic Games (COMP0089)

1:51:24

Lecture 10: Classic Games (COMP0089)

David SilverGoogle DeepMind

Professor

1:51:24·2015·graduate level

This session explores the application of reinforcement learning principles to classic games, detailing methods like Monte Carlo Tree Search (MCTS) and alpha-beta pruning. It illustrates how RL agents can learn optimal strategies in competitive environments with perfect or imperfect information.

AI & MLEconomicsGame Theory & Mechanism DesignReinforcement LearningMonte Carlo Tree SearchAlpha-Beta Pruning

#Reinforcement Learning Course by David Silver# Lecture 10: Classic Games

#Slides and more info about the course: http://goo.gl/vUiyjq

Previous · Lecture 9

Lecture 9: Exploration and Exploitation (COMP0089)

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