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Studying Reinforcement Learning Guide

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Study Reinforcement Learning & (Deep RL) Guide:

  • Simple guide and collective to study RL/DeepRL in one to 2.5 months of time.

Talks to check out first:


  • Introduction to Reinforcement Learning by Joelle Pineau, McGill University:

    • Applications of RL.

    • When to use RL?

    • RL vs supervised learning

    • What is MDP? Markov Decision Process

    • Components of an RL agent:

      • states
      • actions (Probabilistic effects)
      • Reward function
      • Initial state distribution
                                      +-----------------+
               +--------------------- |                 |
               |                      |      Agent      |
               |                      |                 | +---------------------+
               |         +----------> |                 |                       |
               |         |            +-----------------+                       |
               |         |                                                      |
         state |         | reward                                               | action
         S(t)  |         | r(t)                                                 | a(t)
               |         |                                                      |
               |         | +                                                    |
               |         | |  r(t+1) +----------------------------+             |
               |         +-----------+                            |             |
               |           |         |                            | <-----------+
               |           |         |      Environment           |
               |           |  S(t+1) |                            |
               +---------------------+                            |
                           |         +----------------------------+
                           +
        
         * Sutton and Barto (1998)
        
        
    • Explanation of the Markov Property:

    • Why Maximizing utility in:

      • Episodic tasks
      • Continuing tasks
        • The discount factor, gamma γ
    • What is the policy & what to do with it?

      • A policy defines the action-selection strategy at every state:
    • Value functions:

      • The value of a policy equations are (two forms of) Bellman’s equation.
      • (This is a dynamic programming algorithm).
      • Iterative Policy Evaluation:
        • Main idea: turn Bellman equations into update rules.
    • Optimal policies and optimal value functions.

      • Finding a good policy: Policy Iteration (Check the talk Below By Peter Abeel)
      • Finding a good policy: Value iteration
        • Asynchronous value iteration:
        • Instead of updating all states on every iteration, focus on important states.
    • Key challenges in RL:

      • Designing the problem domain
        • State representation – Action choice – Cost/reward signal
      • Acquiring data for training – Exploration / exploitation – High cost actions – Time-delayed cost/reward signal
      • Function approximation
      • Validation / confidence measures
    • The RL lingo.

    • In large state spaces: Need approximation:

      • Fitted Q-iteration:
        • Use supervised learning to estimate the Q-function from a batch of training data:
        • Input, Output and Loss.
          • i.e: The Arcade Learning Environment
    • Deep Q-network (DQN) and tips.

  • Deep Reinforcement Learning by Pieter Abbeel, EE & CS, UC Berkeley

Books:


Courses:


  • Reinforcement Learning by David Silver.

    • Lecture 1: Introduction to Reinforcement Learning
    • Lecture 2: Markov Decision Processes
    • Lecture 3: Planning by Dynamic Programming
    • Lecture 4: Model-Free Prediction
    • Lecture 5: Model-Free Control
    • Lecture 6: Value Function Approximation
    • Lecture 7: Policy Gradient Methods
    • Lecture 8: Integrating Learning and Planning
    • Lecture 9: Exploration and Exploitation
    • Lecture 10: Case Study: RL in Classic Games
  • CS 294: Deep Reinforcement Learning, Spring 2017 by John Schulman and Pieter Abbeel.

    • Instructors: Sergey Levine, John Schulman, Chelsea Finn:
    • My Bad Notes

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Studying Reinforcement Learning Guide

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