Reinforcement learning: an introduction
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Massachusetts ; London, England
The MIT Press
[2018]
|
| Ausgabe: | Second edition |
| Schriftenreihe: | Adaptive computation and machine learning
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | xxii, 526 Seiten Diagramme |
| ISBN: | 9780262039246 |
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| adam_text | Contents Preface to the Second Edition xiii Preface to the First Edition xvii Summary of Notation 1 1 Introduction 1.1 Reinforcement Learning .................................................................... 1.2 Examples .............................................................. 1.3 Elements of Reinforcement Learning......................................... 1.4 Limitations and Scope............................................... 1.5 An Extended Example: Tic-Tac-Toe ............................................ 1.6 Summary........................................................................................ 1.7 Early History of Reinforcement Learning............... Tabular Solution Methods 2 Multi-armed Bandits 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 xix 1 1 4 6 7 8 13 13 23 25 A fc-armed Bandit Problem ..................... . 25 Action-value Methods ................................................................... . 27 The 10-armed Testbed................................................................. 28 Incremental Implementation........... ... 30 Tracking a NonstationaryProblem .............................................................. 32 Optimistic Initial Values............... .. . .................. .. .................... .............. 34 Upper-Confidence-Bound Action Selection ... . . ... . . . . . . 35 Gradient Bandit Algorithms.................... ....... 37 Associative Search (Contextual Bandits)........................ 41 Summary . . ............................................. .. .................... .. . . . ............... 42
viii Contents 3 Finite Markov Decision Processes 47 3.1 The Agent-Environment Interface ............................................................... 47 3.2 Goals and Rewards......................................................................................... 53 3.3 Returns and Episodes................................................................................... 54 3.4 Unified Notation for Episodic and Continuing Tasks................................. 57 3.5 Policies and Value Functions ........................................................... 58 3.6 Optimal Policies and Optimal Value Functions .......................................... 62 3.7 Optimality and Approximation.......................... 67 3.8 Summary................................ 68 4 Dynamic Programming 4.1 Policy Evaluation (Prediction) ................................... 4.2 Policy Improvement ...................................................................................... 4.3 Policy Iteration.................................................. 4.4 Value Iteration........................................................... 4.5 Asynchronous Dynamic Programming....................... 4.6 Generalized Policy Iteration........................................................ 4.7 Efficiency of Dynamic Programming................. 4.8 Summary..................................................................................... 73 74 76 80 82 85 86 87 88 5 Monte Carlo Methods 91 5.1 Monte Carlo Prediction.......................... 92 5.2 Monte Carlo Estimation of Action Values
.......................................... 96 5.3 Monte Carlo Control............ .. ................. ... . . ............ ... 97 5.4 Monte Carlo Control without Exploring Starts ........................... 100 5.5 Off-policy Prediction via Importance Sampling............................................ 103 5.6 Incremental Implementation....................... . . ................................ 109 5.7 Off-policy Monte Carlo Control................................ 110 5.8 *Discounting-aware Importance Sampling.......................... 112 5.9 *Per-decision Importance Sampling................. 114 5.10 Summary........................................................ .................... .. . . . . . 115 6 Temporal-Difference Learning 119 6.1 TD Prediction ........................................................ 119 6.2 Advantages of TD Prediction Methods ........................... 124 6.3 Optimality of TD(0) ........................................................ . . . . ............... 126 6.4 Sarsa: On-policy TD Control................. 129 6.5 Q-learning: Off-policy TD Control .................. .. .................. .. . . . . . . 131 6.6 Expected Sarsa . . . . . . . ............... .. .............. .. . . . . .... ... . v 133 6.7 Maximization Bias and Double Learning........................................................ 134 6.8 Games, Afterstates, and Other Special Cases............................................... 136 6.9 Summary...................................................................................... 138
Contents ix 7 ո-step Bootstrapping 141 7.1 n-step TD Prediction............... .. . . .................. ...... . . . =. .142 7.2 n-step Sarsa . . ... ..... . . ....................................... .·.... . .’. 145 7.3 ո-step Off-policy Learning.................. . ........................... . .148 7.4 *Per-decision Methods with Control Variates............................................ 150 7.5 Off-policy Learning Without Importance Sampling: The ո-step Tree Backup Algorithm ........... . . ..... . . . . 152 7.6 *A Unifying Algorithm: ո-step Q(a)............ ........ v ...... : 154 7.7 Summary . .... . . . . . . ·.՛................. . . . . ... ... . . . . . . . 157 8 Planning and Learning with Tabular Methods 159 8.1 Models and Planning.................................................................... 159 8.2 Dyna: Integrated Planning, Acting, and Learning ........................ .............. 161 8.3 When the Model Is Wrong . .... . . ..... . . . ... . . . . . .... 166 8.4 Prioritized Sweeping .............................. 168 8.5 Expected vs. Sample Updates......................................... 172 8.6 Trajectory Sampling .................... . . . .174 8.7 Real-time Dynamic Programming............ .. . . . 177 8.8 Planning at Decision Time. ... ... . . . ... . ՛.՛. . . . . . . ՛՛. . . . . . 180 8.9 Heuristic Search ... . . . . . . . . . . . . . . . . . .՛. ! . . . . 181 8.10 Rollout Algorithms ........................... .. . . . . . .... . . ։ . . . 183 8.11 Monte Carlo Tree Search. . . . . . . . . . . . . ............... .. . !. . . . . . . 185 8.12
Summary of the Chapter................................... ... ... . . . . 188 8.13 Summary of Part I: Dimensions............ .. ................................... .. 189 II Approximate Solution Methods 195 9 On-policy Prediction with Approximation 197 9.1 Value-function Approximation . . . . . . ... ....... . . . . . . .198 9.2 The Prediction Objective (VE) ..................... .. . . .... .... . . . . . 199 9.3 Stochastic-gradient and Semi-gradient Methods ............... 200 9.4 Linear Methods............ ................. . . 204 9.5 Feature Construction for Linear Methods ..... . . . . .... . . . . . 210 9.5.1 Polynomials............................................... . 210 9.5.2 Fourier Basis................... ........................... . ... ..... . . . . 211 9.5.3 Coarse Coding ...... . . ..................... .... . . ... : ... 215 9.5.4 Tile Coding.............. .............. .. ................................. 217 9.5.5 Radial Basis Functions............ .. . . . . . . .... .՝. .... ! . . 221 9.6 Selecting Step-Size Parameters Manually........................................................222 9.7 Nonlinear Function Approximation: Artificial Neural Networks...............223 9.8 Least-Squares TD ........................................................ 228
Contents x 9.9 9.10 : 9.11 9.12 Memory-based Function Approximation............... ... . . . . . . .... 230 Kernel-based Function Approximation........................ . . . . . . 232 Looking Deeper at On-policy Learning: Interest and Emphasis . . . . . . 234 Summary . ...................... . ............................ ... . . . ......................... 236 10 On-policy Control with Approximation 243 10.1 Episodic Semi-gradient Control . . . .... ... . ;. . .՛·. .... ... . . 243 , 10.2 Semi-gradient ո-step Sarsa . . .......................... .. . ;.............247 10.3 Average Reward: A New Problem Setting for Continuing Tasks ................ 249 10.4 Deprecating the Discounted Setting.................................................................253 , 10.5 Differential Semi-gradient ո-step Sarsa . ..... ... ........... 255 i 10.6 Summary ........ ............................................ . . .................. 256 11 *Off-policy Methods with Approximation 257 11.1 Semi-gradient Methods.............................................. ................................. 258 11.2 Examples of Off-policy Divergence ........................................................ . . . 260 11.3 The Deadly Triad.................................................... ............... ... 264 11.4 Linear Value-function Geometry ..... ... .... . ... ........ . 266 11.5 Gradient Descent in the Bellman Error . . . . . . . . . ... . ................. 269 11.6 The Bellman Error is Not Learnable............................ 274 11.7 Gradient-TD
Methods................................................. ... ......................... ... . 278 11.8 Emphatic-TD Methods........................................... ............... ........................... 281 11.9 Reducing Variance................................................................................... ... . . 283 . 11.10 Summary . . ...................... ... 284 12 Eligibility Traces 287 12.1 The A-return................................................................ .................. . . . · · · 288 12.2 TD(A) . . . . . ... ... . ...................... ... . ! . . : . ................ ... . . . 292 12.3 ո-step Truncated λ-return Methods . . . : . ................... . . .... . 295 12.4 Redoing Updates: Online λ-return Algorithm . . ... . ... . . . . . . . 297 . . 12.5 True Online TD(A)............ .................. .................................................... ... . . 299 1?.6 *Dutch Traces in Monte Carlo Learning ......................... ........................... ... 301 .12.7 Sarsa(A) . .................... ľ . . . .... . . ........................... ... . . 303 12.8 Variable A and 7...................... . ......................... .................. ... . . . . . . . 307 12.9 Off-policy Traces with Control Variates......................... 309 12.10 Watkins’s Q(A) to Tree-Backup(A) ............................................... 312 12.11 Stable Off-policy Methods with Traces ............... ........................ ... 314 12.12 Implementation Issues............................................................................. ... 316
12.13 Conclusions............................................................................. ... 317
Contents xi 13 Policy Gradient Methods 321 13.1 Policy Approximation and its Advantages......................՛................... . . 322 13.2 The Policy Gradient Theorem . . ............................... .................................... 324 13.3 REINFORCE: Monte Carlo Policy Gradient ................................................ 326 13.4 REINFORCE with Baseline............................................................. 13.5 Actor-Critic Methods.................................. ... . .................................. ... 331 13.6 Policy Gradient for Continuing Problems ............... ... ... . . . . . . . . 333 13.7 Policy Parameterization for Continuous Actions.................................... 335 13.8 Summary....................................................................... -337 III Looking Deeper 339 14 Psychology 341 14.1 Prediction and Control.................................. ... ..................................... . 342 14.2 Classical Conditioning........................................... ................................. ... . . 343 14.2.1 Blocking and Higher-order Conditioning ............................ ... . 345 14.2.2 The Rescorla-Wagner Model..................................... ... . 346 14.2.3 TheTD Model ..................................... ... . . . ... ........ . 349 14.2.4 TD Model Simulations............................................................................ 350 14.3 Instrumental Conditioning.......................................................................... ... . 357 14.4 Delayed
Reinforcement......................................................................................... 361 14.5 Cognitive Maps ......................................................................................................363 14.6 Habitual and Goal-directed Behavior .................................................................364 14.7 Summary.............................................................................................................. 368 15 Neuroscience 377 15.1 Neuroscience Basics ............................................................................................378 15.2 Reward Signals, Reinforcement Signals, Values, and Prediction Errors . . 380 15.3 The Reward Prediction Error Hypothesis . .................................................... 381 15.4 Dopamine ...............................................................................................................383 15.5 Experimental Support for the Reward Prediction Error Hypothesis .... 387 15.6 TD Error/Dopamine Correspondence............... 390 15.7 Neural Actor-Critic ............................................................................................395 15.8 Actor and Critic Learning Rules ................................. 398 15.9 Hedonistic Neurons...............................................................................................402 15.10 Collective Reinforcement Learning................................................................... 404 15.11 Model-based Methods in the Brain................................................................... 407
15.12 Addiction............................................................................. ... . 409 15.13 Summary.......................................................................... 410 329
xii 16 Applications and Case Studies 16.1 TD-Gammon . . ................ .-16.2 Samuel’s Checkers Player . . . . 16.3 Watson’s Daily-Double Wagering 16.4 Optimizing Memory Control. . . 16.5 Human-level Video Game Play . 16.6 Mastering the Game of Go . . . . 16.6.1 AlphaGo . . ... . . . . 16.6.2 AlphaGo Zero................... 16.7 Personalized Web Services . . . . 16.8 Thermal Soaring......................... Contents 421 421 426 429 432 436 441 444 447 450 453 17 Frontiers 459 17.1 General ValueFunctions and Auxiliary Tasks................................... 459 17.2 Temporal Abstraction via Options................... . . . . . . . .... . . . . 461 17.3 Observations and State ................................................. 464 17.4 Designing Reward Signals . . . . . . . . ... . . . . . ................469 17.5 Remaining Issues ...................... . . . ... . . . . . . . . .............472 17.6 Experimental Support for the Reward Prediction Error Hypothesis .... 475 References 481 Index 519
|
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| series2 | Adaptive computation and machine learning |
| spellingShingle | Sutton, Richard S. Barto, Andrew Reinforcement learning an introduction Künstliche Intelligenz (DE-588)4033447-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4193754-5 |
| title | Reinforcement learning an introduction |
| title_auth | Reinforcement learning an introduction |
| title_exact_search | Reinforcement learning an introduction |
| title_full | Reinforcement learning an introduction Richard S. Sutton and Andrew G. Barto |
| title_fullStr | Reinforcement learning an introduction Richard S. Sutton and Andrew G. Barto |
| title_full_unstemmed | Reinforcement learning an introduction Richard S. Sutton and Andrew G. Barto |
| title_short | Reinforcement learning |
| title_sort | reinforcement learning an introduction |
| title_sub | an introduction |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Künstliche Intelligenz Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030585045&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT suttonrichards reinforcementlearninganintroduction AT bartoandrew reinforcementlearninganintroduction |
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