Planning and Learning with Tabular Methods
A deep dive into planning and learning with tabular methods for Reinforcement Learning, focusing on value iteration and policy iteration.
1 min read
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All TopicsRead Planning and Learning with Tabular Methods Read TD Learning - First step to Unification of Monte Carlo and DP Read Monte Carlo Methods - Learning from Experience Read Dynamic Programming - Planning with a Perfect Model Read Multi-armed Bandits : Introducing evaluative aspect to RL Read From Minimax to Reinforcement Learning : Why RL wins at Tic-Tac-Toe ?
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6 posts with this topic
TD Learning - First step to Unification of Monte Carlo and DP
A deep dive into Temporal Difference (TD) Learning for Reinforcement Learning, an idea that forms basis for modern deep reinforcement learning algorithms
13 min read
FeaturedReinforcement LearningDemo
Monte Carlo Methods - Learning from Experience
A deep dive into Monte Carlo methods for Reinforcement Learning, focusing on learning optimal strategies from raw experience without a model.
16 min read
FeaturedReinforcement LearningDemo
Dynamic Programming - Planning with a Perfect Model
Introduction to Dynamic Programming: Policy Evaluation, Policy Iteration, Value Iteration and Generalized Policy Iteration
16 min read
FeaturedReinforcement LearningDemo
Multi-armed Bandits : Introducing evaluative aspect to RL
Understanding the multi-armed bandit problem and how to solve it using reinforcement learning alongwith interactive demo. Chapter 3 of Reinforcement Learning: An Introduction by Sutton and Barto
11 min read
FeaturedReinforcement LearningDemo
From Minimax to Reinforcement Learning : Why RL wins at Tic-Tac-Toe ?
Understanding how different algorithms plays Tic-Tac-Toe and why RL wins at it. Interactive demo included!
11 min read
Reinforcement LearningDemo