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Intelligent Agents in Reinforcement Learning: Formulating Optimal Policies

Explore how intelligent agents learn through reinforcement to find optimal policies. Understand pros and cons of this approach. Solid foundation for RL concepts, with a focus on empirical learning. Distinguish between observations and states, various ML types (supervised, unsupervised, RL), value-based vs policy search, and dynamic programming vs model-based learning.

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Intelligent Agents in Reinforcement Learning: Formulating Optimal Policies

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  1. Cpt_S 580-03Intelligent Agents (Reinforcement Learning)

  2. Actions: Wave, Stand, Clap • Observations: Colors, Rewards • Goal: find an optimal policy

  3. What is your policy?

  4. What does the world look like?

  5. Formalize the Problem • Knowns • Unknowns

  6. (UP) Wave • (RIGHT) Stand • (DOWN) Clap

  7. Agent can change policy • Representation and Reward as given • Pros: • Just specify goals • can be much less work than programming • unexpected situations • Cons: • Can be slow • need to pick representation & rewards

  8. Goals for Our Class • Solid foundation for RL • More focus on empirical than theoretical

  9. Introductions

  10. Reduced Formalism • Knowns • Unknowns

  11. Observations vs. State • Types of ML: Supervised, Unsupervised, RL • Value-based vs. Policy Search • Dynamic Programming vs. Model Free vs. Model-based (Model-learning) • Explore vs. Exploit

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