Recent Advances in Reinforcement Learning 8th edition by Sertan Girgin, Manuel Loth, Rémi Munos ISBN 3540897232 ‎ 978-3540897231 by Boris Defourny, Damien Ernst, Louis Wehenkel (auth.), Sertan Girgin, Manuel Loth, Rémi Munos, Philippe Preux, Daniil Ryabko (eds.) 9783540897217, 3540897216 instant download after payment.

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ISBN 10: 3540897232
ISBN 13: ‎ 978-3540897231
Author: Sertan Girgin, Manuel Loth, Rémi Munos 

This book constitutes revised and selected papers of the 8th European Workshop on Reinforcement Learning, EWRL 2008, which took place in Villeneuve d'Ascq, France, during June 30 - July 3, 2008.The 21 papers presented were carefully reviewed and selected from 61 submissions. They are dedicated to the field of and current researches in reinforcement learning.

Recent Advances in Reinforcement Learning 8th Table of contents:

1. Invited Talk Abstracts
2. Invited Talk: UCRL and Autonomous Exploration
3. Invited Talk:Increasing Representational Power and ScalingInference in Reinforcement Learning
4. Invited Talk: PRISM – Practical RL: Representation, Interaction, Synthesis, and Mortality
5. Invited Talk: Towards Robust Reinforcement Learning Algorithms
6. Online Reinforcement Learning
7. Automatic Discovery of Ranking Formulas for Playing with Multi-armed Bandits
8. Introduction
9. Multi-armed Bandit Problem and Policies
10. The K-armed Bandit Problem
11. Index-Based Bandit Policies
12. Systematic Search for Good Ranking Formulas
13. A Grammar for Generating Index Functions
14. Generation of Candidate Formula Structures
15. Optimization of Constants
16. Numerical Experiments
17. Experimental Setup
18. Discovered Policies
19. Evaluation of the Discovered Ranking Formulas
20. Conclusions
21. References
22. Goal-Directed Online Learning of Predictive Models
23. Introduction
24. Predictive State Representations
25. Planning in PSRs
26. Online Reinforcement Learning with Predictive Models
27. Algorithm Overview
28. Online Model Learning
29. Policy Optimization
30. Experimental Results
31. Related Work
32. Discussion and Conclusion
33. References
34. Gradient Based Algorithms with Loss Functions and Kernels for Improved On-Policy Control
35. Introduction
36. Related Work
37. Outline
38. Preliminaries and Stochastic Gradient TD Algorithms
39. Markov Decision Processes
40. Residual Gradient TD
41. GTD and Derivatives
42. Residual Gradient Q-Estimation
43. Linear Updates
44. Reproducing Kernel Hilbert Space Updates
45. Model Based Q-Estimation
46. The Objective
47. Optimizing the Approximators
48. Reproducing Kernel Hilbert Space Extension
49. Optimizing the Value Function
50. Experimental Results
51. Setup
52. Discussion of Results
53. Conclusion
54. References
55. Learning and Exploring MDPs
56. Active Learning of MDP Models
57. Introduction
58. Background
59. Reinforcement Learning
60. Model-Based Bayesian Reinforcement Learning
61. Chosen Family of Probability Distributions
62. Active Learning of MDP Models Using BRL
63. Derived Rewards
64. Performance Criteria
65. From Criteria to Rewards
66. Solving BRL with Belief-Dependent Rewards
67. Experiments
68. Experimental Setup
69. Results
70. Conclusion and Future Work
71. References
72. Handling Ambiguous Effects in Action Learning
73. Introduction
74. Formal Setting
75. Most Likely Actions
76. Variance of Sets of Observations
77. Restriction to Intersections of Intervals
78. Application: Learning Conditions of Actions
79. Conclusion
80. References
81. Feature Reinforcement Learning in Practice
82. Introduction
83. Markov Decision Processes (MDP)
84. Feature Reinforcement Learning
85. Context Trees
86. Stochastic Search
87. The MDP Algorithm
88. Experiments
89. Conclusions
90. References
91. Function Approximation Methods for Reinforcement Learning
92. Reinforcement Learning with a Bilinear Q Function
93. Introduction
94. The Bilinear Representation of the Q Function
95. Fitted Q Iteration
96. Learning the Matrix W
97. Mountain Car Experiments
98. Inventory Management Experiments
99. Discussion
100. References
101. l 1-Penalized Projected Bellman Residual
102. Introduction
103. Preliminaries
104. LSTD
105. LARS-TD
106. 1-penalized Projected Bellman Residual
107. Practical Algorithm
108. Correctness of 1-PBR
109. Discussion
110. Illustration
111. The Two-State MDP
112. The Boyan Chain
113. Conclusion
114. References
115. Regularized Least Squares Temporal Difference Learning with Nested l2 and l 1 Penalization
116. Introduction
117. Preliminaries
118. Regularized LSTD
119. 2 Penalization (L2)
120. 1 Penalization (L1)
121. 2 and 2 Penalization (L22)
122. 2 and 1 Penalization (L21)
123. Standardizing the Data
124. Discussion of the Different Regularization Schemes
125. Experimental Results
126. Conclusion
127. References
128. Recursive Least-Squares Learning with Eligibility Traces
129. Introduction
130. Background and State-of-the-art On-policy Algorithms
131. Extension to Eligibility Traces and Off-policy Learning
132. Off-policy LSTD()
133. Off-policy LSPE()
134. Off-policy FPKF()
135. Off-policy BRM()
136. Illustration of the Algorithms
137. Conclusion
138. References
139. Value Function Approximation through Sparse Bayesian Modeling
140. Introduction
141. Markov Decision Processes and GPTD
142. The Proposed Method
143. Incremental Optimization
144. Working in Episodic Tasks and Unknown Environments
145. Experimental Results
146. Experiments on Simulated Environments
147. Experiments on a Mobile Robot
148. Conclusions
149. References
150. Macro-actions in Reinforcement Learning
151. Automatic Construction of Temporally Extended Actions for MDPs Using Bisimulation Metrics
152. Introduction
153. Background and Notation
154. MDPs and Q-Learning
155. Options
156. Bisimulation Metrics
157. Option Construction
158. Constructing os
159. Constructing os
160. Constructing the Initiation set Ios
161. Empirical Evaluation
162. Rooms World
163. Maze Domain
164. Conclusion and Future Work
165. References
166. Unified Inter and Intra Options Learning Using Policy Gradient Methods
167. Introduction
168. Model and Background
169. Natural Policy Gradient
170. The Options Framework
171. The Augmented Options Model
172. Overall Policy (OP) Description
173. The Augmented Model
174. Natural Gradient of the AHP
175. Multilevel Decision Hierarchies
176. Experimental Results – Inverted Pendulum
177. Concluding Remarks
178. References
179. Options with Exceptions
180. Introduction
181. Notation and Background
182. Notation
183. Option
184. Policy Representation
185. Transition Time Model
186. Identification of Landmark
187. Construction and Updation of Transition Time Model
188. Identification of Exception State
189. Experiment and Results
190. Conclusion
191. References
192. Policy Search and Bounds
193. Robust Bayesian Reinforcement Learning through Tight Lower Bounds
194. Setting
195. Bayes-Optimal Policies
196. Related Work and Main Contribution
197. MMBI: Multi-MDP Backwards Induction
198. Computational Complexity
199. Application to Robust Bayesian Reinforcement Learning
200. Experiments in Reinforcement Learning Problems
201. Discussion
202. References
203. Optimized Look-ahead Tree Search Policies
204. Introduction
205. Problem Formulation
206. Optimal Control Problem
207. Look-ahead Tree Exploration Based Control Policies
208. Budget Constrained Path-Scoring Based Tree Exploration
209. Optimized Look-ahead Tree Exploration Based Control
210. Generic Optimized Look-ahead Tree Exploration Algorithm
211. A Particular Instance
212. Experiments
213. Path Features Function
214. Baselines and Parameters
215. Synthetic Problem
216. HIV Infection Control
217. Conclusion and Further Work
218. References
219. A Framework for Computing Bounds for the Return of a Policy
220. Introduction
221. Framework Description
222. Implementation for Lipschitz Continuity
223. Notation and Assumptions
224. Previous Work
225. Framework Instantiation
226. Discussion of Bounds Based on Lipschitz Continuity
227. Empirical results
228. Deterministic Problems with Unknown Model and Lipschitz Continuous Dynamics
229. Stochastic Problem with known Model
230. Discussion and Future Work
231. References
232. Multi-Task and Transfer Reinforcement Learning
233. Transferring Evolved Reservoir Features in Reinforcement Learning Tasks
234. Introduction
235. Background
236. Echo State Networks
237. NeuroEvolution of Augmented Reservoirs
238. Transfer of Reservoir Topologies
239. Domains
240. Mountain Car
241. Server Job Scheduling
242. Experiments
243. Related Work
244. Conclusions and Future Work
245. References
246. Transfer Learning via Multiple Inter-task Mappings
247. Introduction
248. Transfer via Multiple Inter-task Mappings
249. Transferring with Multiple Inter-task Mappings in Model Based Learners
250. Multiple Inter-task Mappings in TD Learners
251. Domains
252. Mountain Car
253. Keepaway
254. Experiments and Results
255. Transferring with COMBREL in Mountain Car 4D
256. Transferring with Value-Addition in Keepaway
257. Related Work
258. Conclusions and Future Work
259. References
260. Multi-Task Reinforcement Learning: Shaping and Feature Selection
261. Introduction
262. Background and Notation
263. Approximating the Optimal Shaping Function
264. Initialization Closest to Q*m
265. Initialization Closest to m
266. Best Fixed Cross-Task Policy
267. Averaging MDP
268. Shaping Function Evaluation
269. Domain
270. Method
271. Results
272. Shaping Function Representations
273. Evaluation of Representations
274. Feature Relevance
275. Generalization
276. Conclusion
277. References
278. Multi-Agent Reinforcement Learning
279. Transfer Learning in Multi-Agent Reinforcement Learning Domains
280. Introduction
281. Transfer Learning in RL
282. MARL Transfer
283. Intertask Mappings across Multi-Agent Tasks
284. Level of Transferred Knowledge
285. Method of Transfer
286. Experiments
287. Domain
288. Experimental Setup
289. Results and Discussion
290. Conclusions and Future Work
291. References
292. An Extension of a Hierarchical Reinforcement Learning Algorithm for Multiagent Settings
293. Introduction
294. Methodology
295. Taxi Problems
296. MAXQ Hierarchical Decomposition in the Taxi Domain
297. Multiagent Extensions That Use the MAXQ Hierarchy
298. Results
299. Single-Agent Tasks
300. Multiagent Tasks
301. Discussion and Conclusions
302. References
303. Apprenticeship and Inverse Reinforcement Learning
304. Bayesian Multitask Inverse Reinforcement Learning
305. Introduction
306. The General Model
307. Multitask Priors on Reward Functions and Policies
308. Multitask Reward-Policy Prior (MRP)
309. The Policy Prior
310. Reward Priors
311. Estimation
312. Multitask Policy Optimality Prior (MPO)
313. Experiments
314. Related Work and Discussion
315. References
316. Batch, Off-Policy and Model-Free Apprenticeship Learning
317. Introduction
318. Background
319. LSTD-
320. Experimental Benchmark
321. Experiment Description and Results
322. Discussion about the Quality Criterion
323. Conclusion
324. References
325. Real-World Reinforcement Learning
326. Introduction of Fixed Mode States into Online Profit Sharing and Its Application to Waist Trajector
327. Introduction
328. Problem Domain
329. Introduction of Fixed Mode States into Online PS
330. Profit Sharing
331. Online PS
332. Fixed Mode State on Online PS for Long-Term Task
333. Rule Decomposition
334. Action Selection in Online-PS
335. Overall Algorithm for Our Proposal
336. Learning of Biped Walking Robot Waist Trajectory
337. States for Learning
338. Definition of Actions and Modifying Waist Trajectory
339. Rewards and Penalties
340. Simulation Results
341. Learning Schedule
342. Simulation (1) : Effect of Strategy 1 for Fixed Mode State
343. Simulation (2) : Effect of Strategy 2 for Fixed Mode State
344. Conclusions
345. References
346. MapReduce for Parallel Reinforcement Learning
347. Introduction
348. MapReduce
349. MapReduce for Tabular DP and RL
350. Policy Evaluation
351. Policy Iteration
352. Off-policy Updates
353. Tabular Online Algorithms
354. MapReduce for RL: Linear Function Approximation
355. Model-Based Projection
356. Least-Squares Policy Iteration
357. Temporal Difference Learning
358. Conclusions
359. References
360. Compound Reinforcement Learning: Theory and an Application to Finance
361. Introduction
362. Compound Return
363. Compound RL
364. Compound Q-Learning
365. Experimental Results
366. Two-Armed Bandit
367. Global Bond Selection
368. Discussion and Related Work
369. Conclusion
370. References
371. Proposal and Evaluation of the Active Course Classification Support System with Exploitation-Orient
372. Introduction
373. Outline of the Degree-Awarding by NIAD-UE
374. Course Classification Support System
375. Construction of myDB
376. CCS and Its Features
377. The Active Course Classification Support System
378. Features on ACCS
379. Proposal of ACCS with Exploitation-Oriented Learning
380. Incompleteness of Thereshold Learning
381. Learning by Exploitation-Oriented Learning
382. Overall Procedure of ACCS with XoL
383. Evaluation of ACCS with XoL
384. Learning a Policy by XoL or RL
385. Experimental Results
386. Discussion
387. Conclusions
388. References
389. Author Index

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