Reinforcement learning: an introduction
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| Main Authors: | , |
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| Format: | Book |
| Language: | English |
| Published: |
Cambridge, Massachusetts ; London, England
The MIT Press
[2018]
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| Edition: | Second edition |
| Series: | Adaptive computation and machine learning
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Hier auch später erschienene, unveränderte Nachdrucke |
| Physical Description: | xxii, 526 Seiten Diagramme |
| ISBN: | 9780262039246 |
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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 |
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