Reinforcement Learning Foundations

Contents

• Introduction Introduction

  • Reinforcement learning in a nutshell

    59s
• 1. Getting Started with Reinforcement Learning 1. Getting Started with Reinforcement Learning

  • Terms in reinforcement learning

    2m 18s
  • A basic RL problem

    4m 26s
  • Markov decision process

    4m 24s
  • A basic RL solution

    2m 8s
• 2. Reinforcement Learning Algorithms 2. Reinforcement Learning Algorithms

  • Monte Carlo method

    3m 1s
  • Temporal difference methods

    1m 9s
  • Other RL algorithms

    32s
• 3. Monte Carlo Method 3. Monte Carlo Method

  • The setting

    1m 18s
  • Exploration and exploitation

    2m 23s
  • Monte Carlo prediction

    1m 41s
  • First visit and every visit MC prediction

    1m 39s
  • Monte Carlo control

    51s
  • Additional modifications

    2m 55s
• 4. Temporal Difference Methods 4. Temporal Difference Methods

  • The setting

    1m 10s
  • SARSA

    4m 37s
  • SARSAMAX (Q-learning)

    2m 2s
  • Expected SARSA

    1m 46s
• 5. Modified Forms of Reinforcement 5. Modified Forms of Reinforcement

  • Deep reinforcement learning

    1m 20s
  • Multi-agent reinforcement learning

    1m 5s
  • Inverse reinforcement learning

    1m 2s
• Conclusion Conclusion

  • Your reinforcement learning journey

    1m 43s