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Optimizing Behavior: Reinforcement Learning Utility Theory Overview

Explore the fundamental concepts of reinforcement learning utility theory, including examples in animal learning and backgammon. Learn about passive and active learning strategies, Q-functions, exploration methods, and value function approximation in Markov Decision Processes. Discover how empirical model learning and model-based learning can improve decision-making processes in various scenarios.

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Optimizing Behavior: Reinforcement Learning Utility Theory Overview

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  1. CHAPTER 10 Reinforcement LearningUtility Theory

  2. QUESTION????

  3. Recap: MDPs

  4. Reinforcement Learning

  5. Example: Animal Learning • RL studied experimentally for more than 60 years in psychology  Rewards: food, pain, hunger, drugs, etc.  Mechanisms and sophistication debated • Example: foraging  Bees learn near-optimal foraging plan in field of artificial flowers with controlled nectar supplies  Bees have a direct neural connection from nectar intake measurement to motor planning area

  6. Example: Backgammon

  7. Passive Learning

  8. Example: Direct Estimation

  9. Model-Based Learning • Idea:  Learn the model empirically (rather than values) •  Solve the MDP as if the learned model were correct • Empirical model learning  Simplest case:  Count outcomes for each s,a  Normalize to give estimate of T(s,a,s’)  Discover R(s) the first time we enter s • More complex learners are possible (e.g. if we know that all squares have related action outcomes “stationary noise”)

  10. Example: Model-Based Learning

  11. Model-Free Learning

  12. (Greedy) Active Learning • In general, want to learn the optimal policy Idea: • Learn an initial model of the environment: • Solve for the optimal policy for this model (value or policy iteration) • Refine model through experience and repeat

  13. Example: Greedy Active Learning

  14. Q-Functions

  15. Learning Q-Functions: MDPs

  16. Q-Learning

  17. Exploration / Exploitation

  18. Exploration Functions

  19. Function Approximation • Problem: too slow to learn each state’s utility one by one • Solution: what we learn about one state should generalize to similar states • Very much like supervised learning • If states are treated entirely independently, we can only learn on very small state spaces

  20. Discretization

  21. Linear Value Functions

  22. TD Updates for Linear Values

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