Reinforcement Learning — Finite Markov Decision Processes and Dynamic Programming | Computer Science Online Tutorial

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Reinforcement Learning

A self-paced online tutorial session on finite markov decision processes and dynamic programming, part of the Reinforcement Learning track. Work through the topics below, then reach out any time if you'd like a live walkthrough.

Estimated session length: 9 hours Format: Online, self-paced

What this tutorial covers

  • 01The Agent-Environment Interface
  • 02Goals and Rewards
  • 03Finite Markov Decision Process
  • 04The Agent-Environment Interface
  • 05Goals and Rewards
  • 06Returns and Episodes
  • 07Unified Notation for Episodic and Continuing Tasks
  • 08Policies and Value Functions
  • 09Optimal Policies and Optimal Value Functions
  • 10Optimality and Approximation
  • 11Dynamic Programming
  • 12Policy Evaluation (Prediction)
  • 13Policy Improvement
  • 14Policy Iteration
  • 15Value Iteration
  • 16Generalized Policy Iteration
  • 17Efficiency of Dynamic Programming
  • 18Asynchronous Dynamic Programming T3 Policies and value functions for Gridworld example T4 Policy evaluation for Gridworld example

How to use this session

Read through each topic in order — they build on one another. Keep a notes document open and try to restate each concept in your own words before moving to the next one; that single habit does more for retention than re-reading ever will.

If any topic above needs a deeper explanation, a worked example, or a live session with an instructor, get in touch using the details below and we'll set up a time that works for you.

Need help with this topic?

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