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About CMPUT 605
About CMPUT 653 (OLD)
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Lecture Notes
Planning in MDPs
1. Introductions
2. The Fundamental Theorem
3. Value Iteration and Our First Lower Bound
4. Policy Iteration
5. Online Planning - Part I.
6. online planning - Part II.
7. Function Approximation
8. Approximate Policy Iteration
9. Limits of query-efficient planning
10. Planning under $q^*$ realizability
11. Planning under $v^*$ realizability (TensorPlan I.)
12. TensorPlan and eluder sequences
13. From API to Politex
14. Politex
15. From policy search to policy gradients
16. Policy gradients
Online RL
22. Introduction
23. Tabular MDPs
24. Featurized MDPs
Winter 2022 Lecture Notes
Planning in MDPs
1. Introductions
2. The Fundamental Theorem
3. Value Iteration and Our First Lower Bound
4. Policy Iteration
5. Online Planning - Part I.
6. online planning - Part II.
7. Function Approximation
8. Approximate Policy Iteration
9. Limits of query-efficient planning
10. Planning under $q^*$ realizability
11. Planning under $v^*$ realizability (TensorPlan I.)
12. TensorPlan and eluder sequences
13. From API to Politex
14. Politex
15. From policy search to policy gradients
16. Policy gradients
Batch RL
17. Introduction
18. Sample complexity in finite MDPs
19. Scaling with value function approximation
Online RL
22. Introduction
23. Tabular MDPs
24. Featurized MDPs
Winter 2021 Lecture Notes
Planning in MDPs
1. Introductions
2. The Fundamental Theorem
3. Value Iteration and Our First Lower Bound
4. Policy Iteration
5. Local Planning - Part I.
6. Local Planning - Part II.
7. Function Approximation
8. Approximate Policy Iteration
9. Limits of query-efficient planning
10. Planning under $q^*$ realizability
11. Planning under $v^*$ realizability (TensorPlan I.)
12. TensorPlan and eluder sequences
13. From API to Politex
14. Politex
15. From policy search to policy gradients
16. Policy gradients
Batch RL
17. Introduction
18. Sample complexity in finite MDPs
Online RL
Blank
Batch RL
PDF Version
Table of contents
17. Introduction
18. Sample complexity in finite MDPs
19. Scaling with value function approximation