Recent Advances in Reinforcement Learning

Leslie Pack Kaelbling

Springer, Aug 28, 2007 - Computers - 292 pages

Recent Advances in Reinforcement Learning addresses current research in an exciting area that is gaining a great deal of popularity in the Artificial Intelligence and Neural Network communities.
Reinforcement learning has become a primary paradigm of machine learning. It applies to problems in which an agent (such as a robot, a process controller, or an information-retrieval engine) has to learn how to behave given only information about the success of its current actions. This book is a collection of important papers that address topics including the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques. These papers build on previous work and will form an important resource for students and researchers in the area.
Recent Advances in Reinforcement Learning is an edited volume of peer-reviewed original research comprising twelve invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 22, Numbers 1, 2 and 3).

Contents

• 0051pdf: 6
• 0061pdf: 7
• 0071pdf: 8
• 0081pdf: 9
• 0091pdf: 10
• 0111pdf: 11
• 0121pdf: 12
• 0131pdf: 13
• 0141pdf: 14
• 0151pdf: 15
• 0161pdf: 16
• 0171pdf: 17
• 0181pdf: 18
• 0191pdf: 19
• 0201pdf: 20
• 0211pdf: 21
• 0221pdf: 22
• 0231pdf: 23
• 0241pdf: 24
• 0251pdf: 25
• 0261pdf: 26
• 0271pdf: 27
• 0281pdf: 28
• 0291pdf: 29
• 0301pdf: 30
• 0311pdf: 31
• 0321pdf: 32
• 0331pdf: 33
• 0341pdf: 34
• 0351pdf: 35
• 0361pdf: 36
• 0371pdf: 37
• 0381pdf: 38
• 0391pdf: 39
• 0401pdf: 40
• 0411pdf: 41
• 0421pdf: 42
• 0431pdf: 43
• 0441pdf: 44
• 0451pdf: 45
• 0461pdf: 46
• 0471pdf: 47
• 0481pdf: 48
• 0491pdf: 49
• 0501pdf: 50
• 0511pdf: 51
• 0521pdf: 52
• 0531pdf: 53
• 0541pdf: 54
• 0551pdf: 55
• 0561pdf: 56
• 0571pdf: 57
• 0591pdf: 59
• 0601pdf: 60
• 0611pdf: 61
• 0621pdf: 62
• 0631pdf: 63
• 0641pdf: 64
• 0651pdf: 65
• 0661pdf: 66
• 0671pdf: 67
• 0681pdf: 68
• 0691pdf: 69
• 0701pdf: 70
• 0711pdf: 71
• 0721pdf: 72
• 0731pdf: 73
• 0741pdf: 74
• 0751pdf: 75
• 0761pdf: 76
• 0771pdf: 77
• 0781pdf: 78
• 0791pdf: 79
• 0801pdf: 80
• 0811pdf: 81
• 0821pdf: 82
• 0831pdf: 83
• 0841pdf: 84
• 0851pdf: 85
• 0861pdf: 86
• 0871pdf: 87
• 0881pdf: 88
• 0891pdf: 89
• 0901pdf: 90
• 0911pdf: 91
• 0921pdf: 92
• 0931pdf: 93
• 0941pdf: 94
• 0951pdf: 95
• 0961pdf: 96
• 0971pdf: 97
• 0981pdf: 98
• 0991pdf: 99
• 1001pdf: 100
• 1011pdf: 101
• 1021pdf: 102
• 1031pdf: 103
• 1041pdf: 104
• 1051pdf: 105
• 1061pdf: 106
• 1071pdf: 107
• 1081pdf: 108
• 1091pdf: 109
• 1101pdf: 110
• 1111pdf: 111
• 1121pdf: 112
• 1131pdf: 113
• 1141pdf: 114
• 1151pdf: 115
• 1161pdf: 116
• 1171pdf: 117
• 1181pdf: 118
• 1191pdf: 119
• 1201pdf: 120
• 1211pdf: 121
• 1231pdf: 122
• 1241pdf: 124
• 1251pdf: 125
• 1261pdf: 126
• 1271pdf: 127
• 1281pdf: 128
• 1291pdf: 129
• 1301pdf: 130
• 1311pdf: 131
• 1321pdf: 132
• 1331pdf: 133
• 1341pdf: 134
• 1351pdf: 135
• 1361pdf: 136
• 1371pdf: 137
• 1381pdf: 138
• 1391pdf: 139
• 1401pdf: 140
• 1411pdf: 141
• 1421pdf: 142
• 1431pdf: 143
• 1441pdf: 144
• 1451pdf: 145
• 1461pdf: 146
• 1471pdf: 147
• 1481pdf: 148
• 1491pdf: 149
• 1501pdf: 150
• 1511pdf: 151
• 1521pdf: 152
• 1531pdf: 153
• 1541pdf: 154
• 1551pdf: 155
• 1561pdf: 156
• 1571pdf: 157
• 1581pdf: 158
• 1591pdf: 159
• 1601pdf: 160
• 1611pdf: 161
• 1621pdf: 162
• 1631pdf: 163
• 1641pdf: 164
• 1651pdf: 165
• 1661pdf: 166
• 1671pdf: 167
• 1681pdf: 168
• 1691pdf: 169
• 1701pdf: 170
• 1711pdf: 171
• 1721pdf: 172
• 1731pdf: 173
• 1741pdf: 174
• 1751pdf: 175
• 1761pdf: 176
• 1771pdf: 177
• 1781pdf: 178
• 1791pdf: 179
• 1801pdf: 180
• 1811pdf: 181
• 1821pdf: 182
• 1831pdf: 183
• 1841pdf: 184
• 1851pdf: 185
• 1861pdf: 186
• 1871pdf: 187
• 1881pdf: 188
• 1891pdf: 189
• 1901pdf: 190
• 1911pdf: 191
• 1921pdf: 192
• 1931pdf: 193
• 1941pdf: 194
• 1951pdf: 195
• 1971pdf: 197
• 1981pdf: 198
• 1991pdf: 199
• 2001pdf: 200
• 2011pdf: 201
• 2021pdf: 202
• 2031pdf: 203
• 2041pdf: 204
• 2051pdf: 205
• 2061pdf: 206
• 2071pdf: 207
• 2081pdf: 208
• 2091pdf: 209
• 2101pdf: 210
• 2111pdf: 211
• 2121pdf: 212
• 2131pdf: 213
• 2141pdf: 214
• 2151pdf: 215
• 2161pdf: 216
• 2171pdf: 217
• 2181pdf: 218
• 2191pdf: 219
• 2201pdf: 220
• 2211pdf: 221
• 2221pdf: 222
• 2231pdf: 223
• 2241pdf: 224
• 2251pdf: 225
• 2271pdf: 226
• 2281pdf: 228
• 2291pdf: 229
• 2301pdf: 230
• 2311pdf: 231
• 2321pdf: 232
• 2331pdf: 233
• 2341pdf: 234
• 2351pdf: 235
• 2361pdf: 236
• 2371pdf: 237
• 2381pdf: 238
• 2391pdf: 239
• 2401pdf: 240
• 2411pdf: 241
• 2421pdf: 242
• 2431pdf: 243
• 2441pdf: 244
• 2451pdf: 245
• 2461pdf: 246
• 2471pdf: 247
• 2481pdf: 248
• 2491pdf: 249
• 2501pdf: 250
• 2511pdf: 251
• 2521pdf: 252
• 2531pdf: 253
• 2541pdf: 254
• 2551pdf: 255
• 2561pdf: 256
• 2571pdf: 257
• 2581pdf: 258
• 2591pdf: 259
• 2601pdf: 260
• 2611pdf: 261
• 2621pdf: 262
• 2631pdf: 263
• 2641pdf: 264
• 2651pdf: 265
• 2661pdf: 266
• 2671pdf: 267
• 2681pdf: 268
• 2691pdf: 269
• 2701pdf: 270
• 2711pdf: 271
• 2721pdf: 272
• 2731pdf: 273
• 2741pdf: 274
• 2751pdf: 275
• 2761pdf: 276
• 2771pdf: 277
• 2781pdf: 278
• 2791pdf: 279
• 2801pdf: 280
• 2811pdf: 281
• 2831pdf: 283
• 2841pdf: 284
• 2851pdf: 285
• 2861pdf: 286
• 2871pdf: 287
• 2881pdf: 288
• 2891pdf: 289
• 2901pdf: 290
• 2911pdf: 291
• 2921pdf: 292

Popular passages

Page 8 - Reinforcement learning is the learning of a mapping from situations to actions so as to maximize a scalar reward or reinforcement signal.‎

Bibliographic information

• Title: Recent Advances in Reinforcement Learning
• Editor: Leslie Pack Kaelbling
• Edition: illustrated
• Publisher: Springer, 2007
• ISBN: 0585336563, 9780585336565
• Length: 292 pages
• Subjects: Computers / Artificial Intelligence / General; Computers / Computer Science; Computers / Information Technology; Science / Physics / General; Science / Physics / Mathematical & Computational