| 000 | 01104nam a22001697a 4500 | ||
|---|---|---|---|
| 003 | OSt | ||
| 005 | 20250624095344.0 | ||
| 008 | 250624b |||||||| |||| 00| 0 eng d | ||
| 020 | _a9781032708119 | ||
| 082 |
_a006.31 _bSUG |
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| 100 | _aSugiyama, Masashi | ||
| 245 | _aStatistical Reinforcement Learning : Modern Machine Learning Approaches | ||
| 260 |
_aNew York _bCRC Press _c2024 |
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| 300 |
_axiv, 192p. _c23 x 15 cm |
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| 520 | _aI. Introduction 1. Introduction to Reinforcement Learning II. Model-Free Policy Iteration 2. Policy Iteration with Value Function Approximation 3. Basis Design for Value Function Approximation 4. Sample Reuse in Policy Iteration 5. Active Learning in Policy Iteration 6. Robust Policy Iteration III. Model-Free Policy Search 7. Direct Policy Search by Gradient Ascent 8. Direct Policy Search by Expectation-Maximization 9. Policy-Prior Search IV. Model-Based Reinforcement Learning 10. Transition Model Estimation Dimensionality Reduction for Transition Model Estimation | ||
| 942 |
_2ddc _cREF |
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| 999 |
_c6420 _d6420 |
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