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Reinforcement Learning

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Topics & Syllabus 27

#1 Introduction to Reinforcement Learning
An overview of reinforcement learning, its principles, and the difference between sup...
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#2 Markov Decision Processes
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#3 Value Functions and Bellman Equations
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#4 Q-Learning
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#5 Deep Reinforcement Learning
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#6 Policy Gradient Methods
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#7 Exploration vs. Exploitation
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#8 Multi-Armed Bandit Problems
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#9 Reinforcement Learning in Practice
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#10 Reinforcement Learning Algorithms Comparison
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#11 Introduction to Reinforcement Learning
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#12 Markov Decision Processes (MDPs)
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#13 Value Functions
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#14 Policy Iteration and Value Iteration
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#15 Q-Learning
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#16 Deep Reinforcement Learning
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#17 Exploration vs. Exploitation
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#18 Multi-Armed Bandit Problems
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#19 Applications of Reinforcement Learning
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#20 Introduction to Reinforcement Learning
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#21 Markov Decision Processes (MDPs)
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#22 Q-Learning
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#23 Deep Reinforcement Learning
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#24 Exploration vs. Exploitation
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#25 Policy Gradient Methods
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#26 Multi-Armed Bandit Problem
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#27 Applications of Reinforcement Learning
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Unit Outline 40h

Learning Objectives

5 objectives
  • Understand the fundamental principles and components of reinforcement learning (RL) and its position within machine learning paradigms.
  • Gain a thorough understanding of Markov Decision Processes (MDPs) and their role in modeling decision-making problems.
  • Learn and compare key reinforcement learning algorithms including Q-learning, policy gradient methods, and deep reinforcement learning techniques.
  • Explore the exploration vs. exploitation dilemma and strategies to balance them effectively in RL.
  • Analyze real-world applications of reinforcement learning, addressing practical challenges and ethical considerations.

Content Outline

Preview

1. Introduction to Reinforcement Learning

  • Definition and key concepts
    • Agents, environments, states, actions, rewards
    • Interaction loop and learning through trial and error
  • Reinforcement learning vs. supervised and unsupervised learning
  • Components of RL systems
  • Applications overview

2. Markov Decision Processes (MDPs)

  • Formal framework for decision-making
  • Key elements:
    • States
    • Actions
    • Transition probabilities
    • Rewards
  • Markov property
  • Example MDPs

3. Value Functions and Bellman Equations

  • State-value functions (V(s))
  • Action-value functions (Q(s, a))
  • Expected return and discount factor
  • Bellman expectation and optimality equations
  • Role in policy evaluation and improvement

4. Policy Iteration and Value Iteration

  • Policy evaluation
  • Policy improvement
  • Iterative algorithms for solving MDPs
  • Convergence properties

5. Q-Learning

  • Model-free reinforcement learning algorithm
  • Q-value updates and learning rule
  • Exploration strategies (epsilon-greedy)
  • Convergence and theoretical guarantees
  • Practical considerations and limitations

6. Policy Gradient Methods

  • Directly optimizing the policy
  • REINFORCE algorithm
  • Advantages over value-based methods
  • Actor-Critic methods
  • Techniques: advantage functions, entropy regularization

7. Deep Reinforcement Learning

  • Motivation for combining deep learning with RL
  • Deep Q-Networks (DQN)
  • Policy gradients with neural networks
  • Actor-Critic deep RL methods
  • Challenges: stability, sample efficiency

8. Exploration vs. Exploitation

  • The trade-off explained
  • Strategies:
    • Epsilon-greedy
    • Upper Confidence Bound (UCB)
    • Thompson Sampling
  • Multi-armed bandit problem as a simplified model

9. Multi-Armed Bandit Problems

  • Problem formulation
  • Strategies for maximizing cumulative reward
  • Exploration-exploitation algorithms
  • Relation to full RL problems

10. Reinforcement Learning in Practice

  • Real-world applications
    • Robotics
    • Game playing
    • Recommendation systems
    • Finance
    • Healthcare
    • Autonomous driving
  • Challenges in practice
  • Ethical considerations
  • Future directions

11. Reinforcement Learning Algorithms Comparison

  • Summary of algorithms covered
  • Strengths and limitations
  • Use cases and choosing the right approach
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Bachelor of Science in Artificial Intelligence

This unit is an integral part of the Bachelor of Science in Artificial Intelligence syllabus.

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