:Search:

Reinforcement Learning Specialization

Torrent:
Magnet Download .torrent File
Info Hash: E00A4FC3F94EF3FF923884F09A47FFF540D7EE60
Thumbnail:
Similar Posts:
Name Uploaded Size Se Le Upl. by
Reinforcement Learning for Cyber Operations Applications of Artificia… Reinforcement Learning for Cyber Operations Applications of Artificial Intelligence for Penetration
2025-01-07 9.0 MB 11 3 DiamondB
Reinforcement Learning An Introduction Second Edition Reinforcement Learning An Introduction Second Edition
2023-10-23 85.3 MB 14 0 freecoursewb
Reinforcement Learning Specialization Reinforcement Learning Specialization
2023-07-18 4.6 GB 18 4 tutsnode
Reinforcement Learning for Optimal Feedback Control A Lyapunov Reinforcement Learning for Optimal Feedback Control A Lyapunov
2025-06-03 21.9 MB 4 16 freecoursewb
Reinforcement Learning for Business MEAP 01 Reinforcement Learning for Business MEAP 01
2025-09-22 6.3 MB 2 8 freecoursewb
Deep Reinforcement Learning 2022 Edition DevCourseWeb Deep Reinforcement Learning 2022 Edition DevCourseWeb
2023-07-01 63.3 MB 9 3 FreeCourseWeb
Deep Reinforcement Learning with Python With PyTorch TensorFlow and O… Deep Reinforcement Learning with Python With PyTorch TensorFlow and OpenAI Gym by Nimish Sanghi EP
2023-07-01 11.5 MB 40 2 zakareya
Deep Reinforcement Learning for Wireless Networks True DevCourseWeb Deep Reinforcement Learning for Wireless Networks True DevCourseWeb
2023-07-01 10.3 MB 0 4 FreeCourseWeb
Deep Reinforcement Learning and Its Industrial Use Cases AI for Deep Reinforcement Learning and Its Industrial Use Cases AI for
2024-10-22 60.5 MB 8 1 freecoursewb
Deep Reinforcement Learning with Python RLHF for Chatbots and L Deep Reinforcement Learning with Python RLHF for Chatbots and L
2024-07-17 34.1 MB 29 0 freecoursewb
Uploader: tutsnode
Source: T Logo Torrent Galaxy
Downloads: 1001
Language: English
Description:
None
Category: Other
Size: 4.6 GB
Added: July 18, 2023, 7:02 p.m.
Peers: Seeders: 18, Leechers: 4 (Last updated: 10 months, 3 weeks ago)
Tracker Data:
Tracker Seeders Leechers Completed
udp://open.stealth.si:80/announce 5 2 709
udp://exodus.desync.com:6969/announce (Failed to scrape UDP tracker) 0 0 0
udp://tracker.cyberia.is:6969/announce (Failed to scrape UDP tracker) 0 0 0
udp://tracker.opentrackr.org:1337/announce 6 0 237
udp://tracker.torrent.eu.org:451/announce 4 0 45
udp://explodie.org:6969/announce 1 0 0
udp://tracker.birkenwald.de:6969/announce 0 1 0
udp://tracker.moeking.me:6969/announce (Failed to scrape UDP tracker) 0 0 0
udp://ipv4.tracker.harry.lu:80/announce (Failed to scrape UDP tracker) 0 0 0
udp://tracker.tiny-vps.com:6969/announce 2 0 10
udp://tracker.therarbg.to:6969/announce 0 1 0
Files:
  1. 04_warren-powell-approximate-dynamic-programming-for-fleet-management-long.mp4 145.3 MB
  2. TutsNode.net.txt 63 bytes
  3. 01_sequential-decision-making_quiz.html 210.3 KB
  4. 01_course-4-introduction.en.txt 2.3 KB
  5. 01_dynamic-programming_quiz.html 157.5 KB
  6. 04_read-me-pre-requisites-and-learning-objectives_Course_2__Sample_Based_Learning_Methods_Learning_Objectives.pdf 83.1 KB
  7. 06_read-me-pre-requisites-and-learning-objectives_Fundamentals_of_Reinforcement_Learning__Learning_Objectives.pdf 64.7 KB
  8. 03_read-me-pre-requisites-and-learning-objectives_Prediction_and_Control_with_Function_Approximation_Learning_Objectives.pdf 59.9 KB
  9. 03_reinforcement-learning-textbook_instructions.html 2.2 KB
  10. 04_pre-requisites-and-learning-objectives_A_Complete_Reinforcement_Learning_System_Capstone__Learning_Objectives.pdf 56.8 KB
  11. 04_emma-brunskill-batch-reinforcement-learning.en.srt 24.9 KB
  12. 02_course-introduction.en.txt 5.6 KB
  13. 0 14 bytes
  14. 05_reinforcement-learning-textbook_RLbook2018.pdf 85.3 MB
  15. 04_warren-powell-approximate-dynamic-programming-for-fleet-management-long.en.srt 40.7 KB
  16. 02_graded-value-functions-and-bellman-equations_exam.html 31.1 KB
  17. 04_warren-powell-approximate-dynamic-programming-for-fleet-management-long.en.txt 21.3 KB
  18. 02_satinder-singh-on-intrinsic-rewards.en.srt 21.0 KB
  19. 02_michael-littman-the-reward-hypothesis.en.srt 18.5 KB
  20. 03_andy-barto-and-rich-sutton-more-on-the-history-of-rl.en.srt 15.9 KB
  21. 03_meet-your-instructors.en.srt 15.9 KB
  22. 03_lets-review-average-reward-a-new-way-of-formulating-control-problems.en.srt 15.2 KB
  23. 02_lets-review-examples-of-episodic-and-continuing-tasks.en.txt 2.5 KB
  24. 01_average-reward-a-new-way-of-formulating-control-problems.en.srt 15.2 KB
  25. 03_david-silver-on-deep-learning-rl-ai.en.srt 14.7 KB
  26. 01_meeting-with-niko-choosing-the-learning-algorithm.en.txt 2.8 KB
  27. 04_jonathan-langford-contextual-bandits-for-real-world-reinforcement-learning.en.srt 14.0 KB
  28. 01_gradient-descent-for-training-neural-networks.en.srt 14.0 KB
  29. 01_lets-review-expected-sarsa.en.txt 2.8 KB
  30. 04_iterative-policy-evaluation.en.srt 13.7 KB
  31. 02_joelle-pineau-about-rl-that-matters.en.srt 13.7 KB
  32. 02_lets-review-what-is-q-learning.en.txt 2.6 KB
  33. 02_meet-your-instructors.en.srt 13.4 KB
  34. 02_meet-your-instructors.en.srt 13.4 KB
  35. 02_meet-your-instructors.en.srt 13.4 KB
  36. 02_policy-iteration.en.srt 13.3 KB
  37. 04_emma-brunskill-batch-reinforcement-learning.en.txt 13.2 KB
  38. 03_gaussian-policies-for-continuous-actions.en.srt 12.8 KB
  39. 02_andy-barto-on-what-are-eligibility-traces-and-why-are-they-so-named.en.srt 12.5 KB
  40. 01_what-is-the-trade-off.en.srt 12.2 KB
  41. 01_optimal-policies.en.srt 12.2 KB
  42. 03_warren-powell-approximate-dynamic-programming-for-fleet-management-short.en.srt 12.1 KB
  43. 01_mdps_quiz.html 11.8 KB
  44. 02_michael-littman-the-reward-hypothesis.en.txt 11.6 KB
  45. 03_doina-precup-building-knowledge-for-ai-agents-with-reinforcement-learning.en.srt 11.3 KB
  46. 04_rich-sutton-the-importance-of-td-learning.en.srt 11.2 KB
  47. 02_satinder-singh-on-intrinsic-rewards.en.txt 11.0 KB
  48. 02_demonstration-with-actor-critic.en.srt 10.9 KB
  49. 03_using-optimal-value-functions-to-get-optimal-policies.en.srt 10.8 KB
  50. 01_agent-architecture-meeting-with-martha-overview-of-design-choices.en.srt 10.8 KB
  51. 04_using-monte-carlo-for-prediction.en.srt 10.6 KB
  52. 03_what-is-monte-carlo.en.srt 10.5 KB
  53. 02_drew-bagnell-on-system-id-optimal-control.en.srt 10.5 KB
  54. 05_rich-sutton-and-andy-barto-a-brief-history-of-rl.en.srt 10.5 KB
  55. 03_moving-to-parameterized-functions.en.srt 10.4 KB
  56. 02_course-introduction.en.srt 10.4 KB
  57. 03_susan-murphy-on-rl-in-mobile-health.en.srt 10.4 KB
  58. 04_value-functions.en.srt 10.3 KB
  59. 03_drew-bagnell-self-driving-robotics-and-model-based-rl.en.srt 10.3 KB
  60. 04_state-aggregation-with-monte-carlo.en.srt 10.2 KB
  61. 03_learning-policies-directly.en.srt 10.2 KB
  62. 02_introducing-gradient-descent.en.srt 9.9 KB
  63. 01_bellman-equation-derivation.en.srt 9.6 KB
  64. 03_markov-decision-processes.en.srt 9.6 KB
  65. 02_lets-review-expected-sarsa-with-function-approximation.en.txt 2.1 KB
  66. 01_lets-review-markov-decision-processes.en.srt 9.6 KB
  67. 05_csaba-szepesvari-on-problem-landscape.en.srt 9.6 KB
  68. 03_david-silver-on-deep-learning-rl-ai.en.txt 9.5 KB
  69. 01_average-reward-a-new-way-of-formulating-control-problems.en.txt 9.4 KB
  70. 03_lets-review-average-reward-a-new-way-of-formulating-control-problems.en.txt 9.4 KB
  71. 04_generalization-properties-of-coarse-coding.en.srt 9.4 KB
  72. 03_gradient-monte-for-policy-evaluation.en.srt 9.3 KB
  73. 02_the-policy-gradient-theorem.en.srt 9.3 KB
  74. 02_in-depth-with-changing-environments.en.srt 9.2 KB
  75. 02_actor-critic-algorithm.en.srt 9.2 KB
  76. 02_weekly-reading_instructions.html 1.2 KB
  77. 1 3 bytes
  78. 02_weekly-reading_RLbook2018.pdf 85.3 MB
  79. 04_lets-review-actor-critic-algorithm.en.srt 9.2 KB
  80. 01_the-objective-for-learning-policies.en.srt 8.9 KB
  81. 01_course-3-introduction.en.srt 8.9 KB
  82. 02_joelle-pineau-about-rl-that-matters.en.txt 8.8 KB
  83. 03_sequential-decision-making-with-evaluative-feedback.en.srt 8.7 KB
  84. 04_generalization-and-discrimination.en.srt 8.7 KB
  85. 04_episodic-sarsa-in-mountain-car.en.srt 8.7 KB
  86. 01_meeting-with-martha-discussing-your-results.en.txt 2.4 KB
  87. 02_meet-your-instructors.en.txt 8.6 KB
  88. 02_course-wrap-up.en.srt 3.0 KB
  89. 02_course-wrap-up.en.txt 1.8 KB
  90. 02_meet-your-instructors.en.txt 8.6 KB
  91. 02_meet-your-instructors.en.txt 8.6 KB
  92. 02_optimistic-initial-values.en.srt 8.5 KB
  93. 03_meet-your-instructors.en.txt 8.4 KB
  94. 02_optimization-strategies-for-nns.en.srt 8.4 KB
  95. 01_specialization-introduction.en.txt 2.6 KB
  96. 01_lets-review-optimization-strategies-for-nns.en.srt 8.4 KB
  97. 02_optimal-value-functions.en.srt 8.3 KB
  98. 06_using-tile-coding-in-td.en.srt 8.3 KB
  99. 02_the-true-objective-for-td.en.srt 8.2 KB
  100. 05_martin-riedmiller-on-the-collect-and-infer-framework-for-data-efficient-rl.en.srt 8.2 KB
  101. 01_the-advantages-of-temporal-difference-learning.en.srt 8.2 KB
  102. 01_lets-review-comparing-td-and-monte-carlo.en.srt 8.1 KB
  103. 02_comparing-td-and-monte-carlo.en.srt 8.1 KB
  104. 01_meeting-with-adam-parameter-studies-in-rl.en.srt 8.1 KB
  105. 03_andy-barto-and-rich-sutton-more-on-the-history-of-rl.en.txt 8.1 KB
  106. 05_reinforcement-learning-textbook_instructions.html 2.2 KB
  107. 02_estimating-action-values-incrementally.en.srt 8.1 KB
  108. 01_practice-value-functions-and-bellman-equations_quiz.html 8.0 KB
  109. 06_read-me-pre-requisites-and-learning-objectives_instructions.html 2.6 KB
  110. 01_module-1-learning-objectives_instructions.html 2.8 KB
  111. 02_weekly-reading_instructions.html 1.2 KB
  112. 02_the-dyna-algorithm.en.srt 7.8 KB
  113. 03_off-policy-monte-carlo-prediction.en.srt 7.8 KB
  114. 03_what-is-temporal-difference-td-learning.en.srt 7.8 KB
  115. 02_efficiency-of-dynamic-programming.en.srt 7.7 KB
  116. 04_advantages-of-policy-parameterization.en.srt 7.7 KB
  117. 01_continuing-tasks.en.srt 7.6 KB
  118. 01_epsilon-soft-policies.en.srt 7.5 KB
  119. 03_upper-confidence-bound-ucb-action-selection.en.srt 7.5 KB
  120. 03_what-is-a-model.en.srt 7.5 KB
  121. 03_specifying-policies.en.srt 7.5 KB
  122. 03_warren-powell-approximate-dynamic-programming-for-fleet-management-short.en.txt 7.5 KB
  123. 01_estimating-the-policy-gradient.en.srt 7.5 KB
  124. 01_gradient-descent-for-training-neural-networks.en.txt 7.5 KB
  125. 04_meeting-with-martha-in-depth-on-experience-replay.en.srt 7.4 KB
  126. 04_jonathan-langford-contextual-bandits-for-real-world-reinforcement-learning.en.txt 7.3 KB
  127. 03_how-is-q-learning-off-policy.en.srt 7.2 KB
  128. 04_iterative-policy-evaluation.en.txt 7.2 KB
  129. 02_policy-iteration.en.txt 7.1 KB
  130. 01_meeting-with-adam-getting-the-agent-details-right.en.srt 7.1 KB
  131. 01_flexibility-of-the-policy-iteration-framework.en.srt 7.1 KB
  132. 01_what-if-the-model-is-inaccurate.en.srt 7.0 KB
  133. 04_week-4-summary.en.srt 7.0 KB
  134. 02_why-bellman-equations.en.srt 7.0 KB
  135. 05_week-1-summary.en.txt 2.7 KB
  136. 01_learning-action-values.en.srt 7.0 KB
  137. 06_chapter-summary_instructions.html 1.2 KB
  138. 03_gaussian-policies-for-continuous-actions.en.txt 6.9 KB
  139. 01_the-dyna-architecture.en.srt 6.9 KB
  140. 02_bandits-and-exploration-exploitation_instructions.html 1.1 KB
  141. 01_module-2-learning-objectives_instructions.html 2.4 KB
  142. 02_weekly-reading_instructions.html 1.2 KB
  143. 03_lets-review-dyna-q-learning-in-a-simple-maze.en.srt 6.9 KB
  144. 03_dyna-q-learning-in-a-simple-maze.en.srt 6.9 KB
  145. 02_comparing-td-and-monte-carlo-with-state-aggregation.en.srt 6.9 KB
  146. 04_examples-of-mdps.en.srt 6.8 KB
  147. 02_drew-bagnell-on-system-id-optimal-control.en.txt 6.8 KB
  148. 01_initial-project-meeting-with-martha-formalizing-the-problem.en.srt 6.8 KB
  149. 03_using-optimal-value-functions-to-get-optimal-policies.en.txt 6.7 KB
  150. 03_week-1-summary.en.srt 6.7 KB
  151. 01_the-goal-of-reinforcement-learning.en.txt 2.6 KB
  152. 03_drew-bagnell-self-driving-robotics-and-model-based-rl.en.txt 6.7 KB
  153. 03_policy-evaluation-vs-control.en.srt 6.7 KB
  154. 04_your-specialization-roadmap.en.srt 6.6 KB
  155. 02_andy-barto-on-what-are-eligibility-traces-and-why-are-they-so-named.en.txt 6.6 KB
  156. 02_importance-sampling.en.srt 6.6 KB
  157. 01_what-is-the-trade-off.en.txt 6.6 KB
  158. 01_exploration-under-function-approximation.en.srt 6.5 KB
  159. 01_policy-improvement.en.srt 6.5 KB
  160. 02_examples-of-episodic-and-continuing-tasks.en.txt 2.5 KB
  161. 03_solving-the-blackjack-example.en.srt 6.5 KB
  162. 03_week-2-summary.en.srt 2.8 KB
  163. 03_week-2-summary.en.txt 1.5 KB
  164. 01_optimal-policies.en.txt 6.4 KB
  165. 04_week-3-summary.en.srt 6.4 KB
  166. 02_graded-assignment-describe-three-mdps_peer_assignment_instructions.html 2.3 KB
  167. 01_congratulations.en.srt 6.3 KB
  168. 03_susan-murphy-on-rl-in-mobile-health.en.txt 6.3 KB
  169. 01_the-linear-td-update.en.srt 6.3 KB
  170. 05_framing-value-estimation-as-supervised-learning.en.srt 6.3 KB
  171. 01_the-value-error-objective.en.srt 6.2 KB
  172. 04_state-aggregation-with-monte-carlo.en.txt 6.2 KB
  173. 03_episodic-sarsa-with-function-approximation.en.srt 6.2 KB
  174. 02_introducing-gradient-descent.en.txt 6.2 KB
  175. 03_sarsa-gpi-with-td.en.srt 6.1 KB
  176. 01_lets-review-non-linear-approximation-with-neural-networks.en.srt 6.1 KB
  177. 02_non-linear-approximation-with-neural-networks.en.srt 6.1 KB
  178. 03_doina-precup-building-knowledge-for-ai-agents-with-reinforcement-learning.en.txt 6.1 KB
  179. 05_csaba-szepesvari-on-problem-landscape.en.txt 6.1 KB
  180. 01_actor-critic-with-softmax-policies.en.srt 6.0 KB
  181. 01_why-does-off-policy-learning-matter.en.srt 5.9 KB
  182. 04_rich-sutton-the-importance-of-td-learning.en.txt 5.9 KB
  183. 03_deep-neural-networks.en.srt 5.9 KB
  184. 02_demonstration-with-actor-critic.en.txt 5.9 KB
  185. 06_andy-and-rich-advice-for-students.en.srt 5.8 KB
  186. 01_agent-architecture-meeting-with-martha-overview-of-design-choices.en.txt 5.8 KB
  187. 02_q-learning-in-the-windy-grid-world.en.srt 5.8 KB
  188. 03_what-is-monte-carlo.en.txt 5.6 KB
  189. 04_using-monte-carlo-for-prediction.en.txt 5.6 KB
  190. 05_week-1-summary.en.srt 5.6 KB
  191. 03_moving-to-parameterized-functions.en.txt 5.6 KB
  192. 04_value-functions.en.txt 5.5 KB
  193. 01_what-is-a-neural-network.en.srt 5.5 KB
  194. 01__resources.html 5.5 KB
  195. 2 275 bytes
  196. 05_chapter-summary_RLbook2018.pdf 85.3 MB
  197. 05_chapter-summary_instructions.html 1.1 KB
  198. 03_learning-policies-directly.en.txt 5.4 KB
  199. 03_specialization-wrap-up.en.srt 5.4 KB
  200. 05_rich-sutton-and-andy-barto-a-brief-history-of-rl.en.txt 5.4 KB
  201. 01_module-4-learning-objectives_instructions.html 3.0 KB
  202. 02_weekly-reading_instructions.html 1.2 KB
  203. 01_random-tabular-q-planning.en.srt 5.4 KB
  204. 02_optimistic-initial-values.en.txt 5.4 KB
  205. 03_markov-decision-processes.en.txt 5.2 KB
  206. 01_lets-review-markov-decision-processes.en.txt 5.2 KB
  207. 05_tile-coding.en.srt 5.2 KB
  208. 01_bellman-equation-derivation.en.txt 5.1 KB
  209. 05_martin-riedmiller-on-the-collect-and-infer-framework-for-data-efficient-rl.en.txt 5.1 KB
  210. 01_meeting-with-adam-parameter-studies-in-rl.en.txt 5.1 KB
  211. 04_generalization-properties-of-coarse-coding.en.txt 5.0 KB
  212. 02_lets-review-what-is-q-learning.en.srt 4.9 KB
  213. 01_what-is-q-learning.en.srt 4.9 KB
  214. 02_in-depth-with-changing-environments.en.txt 4.9 KB
  215. 01_specialization-introduction.en.srt 4.9 KB
  216. 02_the-policy-gradient-theorem.en.txt 4.9 KB
  217. 02_actor-critic-algorithm.en.txt 4.9 KB
  218. 04_lets-review-actor-critic-algorithm.en.txt 4.9 KB
  219. 03_gradient-monte-for-policy-evaluation.en.txt 4.9 KB
  220. 01_the-goal-of-reinforcement-learning.en.srt 4.9 KB
  221. 03_coarse-coding.en.srt 4.9 KB
  222. 02_efficiency-of-dynamic-programming.en.txt 4.8 KB
  223. 01_epsilon-soft-policies.en.txt 4.8 KB
  224. 01_the-objective-for-learning-policies.en.txt 4.7 KB
  225. 01_using-monte-carlo-for-action-values.en.srt 4.7 KB
  226. 04_advantages-of-policy-parameterization.en.txt 4.7 KB
  227. [TGx]Downloaded from torrentgalaxy.to .txt 585 bytes
  228. 01_course-3-introduction.en.txt 4.7 KB
  229. 05_week-4-summary.en.txt 2.4 KB
  230. 02_lets-review-examples-of-episodic-and-continuing-tasks.en.srt 4.7 KB
  231. 06_chapter-summary_instructions.html 1.2 KB
  232. 02_examples-of-episodic-and-continuing-tasks.en.srt 4.7 KB
  233. 03_week-3-review.en.srt 4.7 KB
  234. 02_optimal-policies-with-dynamic-programming_instructions.html 1.1 KB
  235. 04_meeting-with-martha-in-depth-on-experience-replay.en.txt 4.7 KB
  236. 03_sequential-decision-making-with-evaluative-feedback.en.txt 4.6 KB
  237. 04_episodic-sarsa-in-mountain-car.en.txt 4.6 KB
  238. 01_estimating-the-policy-gradient.en.txt 4.6 KB
  239. 04_generalization-and-discrimination.en.txt 4.6 KB
  240. 01_semi-gradient-td-for-policy-evaluation.en.srt 4.6 KB
  241. 01_meeting-with-niko-choosing-the-learning-algorithm.en.srt 4.6 KB
  242. 01_lets-review-optimization-strategies-for-nns.en.txt 4.5 KB
  243. 02_optimization-strategies-for-nns.en.txt 4.5 KB
  244. 01_lets-review-expected-sarsa.en.srt 4.5 KB
  245. 01_expected-sarsa.en.srt 4.5 KB
  246. 04_reinforcement-learning-textbook_instructions.html 2.2 KB
  247. 02_optimal-value-functions.en.txt 4.5 KB
  248. 05_week-4-summary.en.srt 4.5 KB
  249. 02_weekly-reading-on-policy-prediction-with-approximation_instructions.html 1.2 KB
  250. 02_why-bellman-equations.en.txt 4.4 KB
  251. 01_meeting-with-adam-getting-the-agent-details-right.en.txt 4.4 KB
  252. 05_week-1-summary.en.srt 4.3 KB
  253. 06_using-tile-coding-in-td.en.txt 4.3 KB
  254. 02_the-true-objective-for-td.en.txt 4.3 KB
  255. 01_the-advantages-of-temporal-difference-learning.en.txt 4.3 KB
  256. 02_estimating-action-values-incrementally.en.txt 4.3 KB
  257. 01_the-dyna-architecture.en.txt 4.3 KB
  258. 01_lets-review-comparing-td-and-monte-carlo.en.txt 4.3 KB
  259. 02_comparing-td-and-monte-carlo.en.txt 4.3 KB
  260. 01_course-4-introduction.en.srt 4.2 KB
  261. 01_congratulations-course-4-preview.en.srt 4.2 KB
  262. 01_initial-project-meeting-with-martha-formalizing-the-problem.en.txt 4.2 KB
  263. 03_lets-review-dyna-q-learning-in-a-simple-maze.en.txt 4.2 KB
  264. 03_dyna-q-learning-in-a-simple-maze.en.txt 4.2 KB
  265. 03_policy-evaluation-vs-control.en.txt 4.2 KB
  266. 02_the-dyna-algorithm.en.txt 4.2 KB
  267. 03_off-policy-monte-carlo-prediction.en.txt 4.1 KB
  268. 03_what-is-temporal-difference-td-learning.en.txt 4.1 KB
  269. 04_week-2-review.en.srt 4.1 KB
  270. 03_upper-confidence-bound-ucb-action-selection.en.txt 4.0 KB
  271. 02_using-monte-carlo-methods-for-generalized-policy-iteration.en.srt 4.0 KB
  272. 03_specifying-policies.en.txt 4.0 KB
  273. 01_semi-gradient-td-for-policy-evaluation.en.txt 2.9 KB
  274. 01_course-introduction.en.srt 4.0 KB
  275. 01_continuing-tasks.en.txt 4.0 KB
  276. 03_how-is-q-learning-off-policy.en.txt 4.0 KB
  277. 03_what-is-a-model.en.txt 4.0 KB
  278. 01_module-1-learning-objectives_instructions.html 4.0 KB
  279. 05_expected-sarsa-with-function-approximation.en.srt 3.9 KB
  280. 02_lets-review-expected-sarsa-with-function-approximation.en.srt 3.9 KB
  281. 01_meeting-with-martha-discussing-your-results.en.srt 3.9 KB
  282. 04_sarsa-in-the-windy-grid-world.en.srt 3.9 KB
  283. 04_comparing-sample-and-distribution-models.en.srt 3.9 KB
  284. 03_episodic-sarsa-with-function-approximation.en.txt 3.9 KB
  285. 02_non-linear-approximation-with-neural-networks.en.txt 3.9 KB
  286. 01_lets-review-non-linear-approximation-with-neural-networks.en.txt 3.9 KB
  287. 01_why-does-off-policy-learning-matter.en.txt 3.8 KB
  288. 01_flexibility-of-the-policy-iteration-framework.en.txt 3.8 KB
  289. 01_learning-action-values.en.txt 3.8 KB
  290. 01_what-if-the-model-is-inaccurate.en.txt 3.8 KB
  291. 02_weekly-reading-on-policy-prediction-with-approximation-ii_instructions.html 1.2 KB
  292. 02_expected-sarsa-in-the-cliff-world.en.srt 3.7 KB
  293. 01_actor-critic-with-softmax-policies.en.txt 3.7 KB
  294. 04_examples-of-mdps.en.txt 3.7 KB
  295. 04_week-4-summary.en.txt 3.7 KB
  296. 02_comparing-td-and-monte-carlo-with-state-aggregation.en.txt 3.6 KB
  297. 04_pre-requisites-and-learning-objectives_instructions.html 3.6 KB
  298. 03_week-1-summary.en.txt 3.6 KB
  299. 01_module-4-learning-objectives_instructions.html 3.5 KB
  300. 05_tile-coding.en.txt 2.8 KB
  301. 06_andy-and-rich-advice-for-students.en.txt 3.5 KB
  302. 01_exploration-under-function-approximation.en.txt 3.5 KB
  303. 01_policy-improvement.en.txt 3.5 KB
  304. 02_importance-sampling.en.txt 3.5 KB
  305. 04_your-specialization-roadmap.en.txt 3.5 KB
  306. 01_what-is-a-neural-network.en.txt 3.0 KB
  307. 01_the-value-error-objective.en.txt 3.4 KB
  308. 03_solving-the-blackjack-example.en.txt 3.4 KB
  309. 01_congratulations.en.txt 3.4 KB
  310. 03_specialization-wrap-up.en.txt 3.4 KB
  311. 04_week-3-summary.en.txt 3.4 KB
  312. 05_framing-value-estimation-as-supervised-learning.en.txt 3.3 KB
  313. 01_congratulations.en.srt 3.3 KB
  314. 01_the-linear-td-update.en.txt 3.3 KB
  315. 03_sarsa-gpi-with-td.en.txt 3.2 KB
  316. 01_module-3-learning-objectives_instructions.html 3.2 KB
  317. 03_read-me-pre-requisites-and-learning-objectives_instructions.html 3.2 KB
  318. 03_deep-neural-networks.en.txt 3.2 KB
  319. 04_week-2-summary.en.srt 3.1 KB
  320. 01_module-2-learning-objectives_instructions.html 3.1 KB
  321. 02_q-learning-in-the-windy-grid-world.en.txt 3.0 KB
  322. 01_module-1-learning-objectives_instructions.html 3.0 KB
  323. 03_coarse-coding.en.txt 3.0 KB
  324. 04_week-2-review.en.txt 2.2 KB
  325. 04_read-me-pre-requisites-and-learning-objectives_instructions.html 3.0 KB
  326. 01_module-3-learning-objectives_instructions.html 2.2 KB
  327. 02_weekly-reading-on-policy-control-with-approximation_instructions.html 1.3 KB
  328. 05_week-1-summary.en.txt 3.0 KB
  329. 01_random-tabular-q-planning.en.txt 2.9 KB
  330. 03_generality-of-expected-sarsa.en.srt 2.9 KB
  331. 01_module-4-learning-objectives_instructions.html 2.9 KB
  332. 01_module-3-learning-objectives_instructions.html 2.8 KB
  333. 01_expected-sarsa.en.txt 2.8 KB
  334. 04_week-3-summary.en.srt 2.7 KB
  335. 01_what-is-q-learning.en.txt 2.6 KB
  336. 05_expected-sarsa-with-function-approximation.en.txt 2.1 KB
  337. 04_week-4-summary.en.srt 2.6 KB
  338. 01_using-monte-carlo-for-action-values.en.txt 2.5 KB
  339. 03_week-3-review.en.txt 2.5 KB
  340. 04_sarsa-in-the-windy-grid-world.en.txt 2.4 KB
  341. 02_expected-sarsa-in-the-cliff-world.en.txt 2.3 KB
  342. 01_congratulations-course-4-preview.en.txt 2.3 KB
  343. 03_reinforcement-learning-textbook_instructions.html 2.2 KB
  344. 01_course-introduction.en.txt 2.1 KB
  345. 02_using-monte-carlo-methods-for-generalized-policy-iteration.en.txt 2.1 KB
  346. 04_comparing-sample-and-distribution-models.en.txt 2.1 KB
  347. 01_congratulations.en.txt 2.1 KB
  348. 01_module-2-learning-objectives_instructions.html 1.7 KB
  349. 04_week-2-summary.en.txt 1.7 KB
  350. 04_week-3-summary.en.txt 1.6 KB
  351. 02_weekly-reading-policy-gradient-methods_instructions.html 1.2 KB
  352. 03_generality-of-expected-sarsa.en.txt 1.5 KB
  353. 04_week-4-summary.en.txt 1.4 KB
  354. 05_chapter-summary_instructions.html 1.2 KB
  355. 06_text-book-part-1-summary_instructions.html 1.2 KB
  356. 06_chapter-summary_instructions.html 1.2 KB
  357. 02_weekly-reading_instructions.html 1.2 KB
  358. 02_weekly-reading_instructions.html 1.2 KB
  359. 02_weekly-reading_instructions.html 1.2 KB
  360. 05_chapter-summary_instructions.html 1.2 KB
  361. 02_weekly-reading_instructions.html 1.2 KB
  362. 3 155.0 KB
  363. 06_chapter-summary_RLbook2018.pdf 85.3 MB
  364. 4 734.2 KB
  365. 03_reinforcement-learning-textbook_RLbook2018.pdf 85.3 MB
  366. 5 734.2 KB
  367. 02_weekly-reading_RLbook2018.pdf 85.3 MB
  368. 6 734.2 KB
  369. 04_reinforcement-learning-textbook_RLbook2018.pdf 85.3 MB
  370. 7 734.2 KB
  371. 02_weekly-reading_RLbook2018.pdf 85.3 MB
  372. 8 734.2 KB
  373. 03_reinforcement-learning-textbook_RLbook2018.pdf 85.3 MB
  374. 9 734.2 KB
  375. 02_weekly-reading-on-policy-control-with-approximation_RLbook2018.pdf 85.3 MB
  376. 10 734.2 KB
  377. 06_chapter-summary_RLbook2018.pdf 85.3 MB
  378. 11 734.2 KB
  379. 02_weekly-reading_RLbook2018.pdf 85.3 MB
  380. 12 734.2 KB
  381. 02_weekly-reading_RLbook2018.pdf 85.3 MB
  382. 13 734.2 KB
  383. 02_weekly-reading_RLbook2018.pdf 85.3 MB
  384. 14 734.2 KB
  385. 02_weekly-reading-on-policy-prediction-with-approximation-ii_RLbook2018.pdf 85.3 MB
  386. 15 734.2 KB
  387. 02_weekly-reading-policy-gradient-methods_RLbook2018.pdf 85.3 MB
  388. 16 734.2 KB
  389. 05_chapter-summary_RLbook2018.pdf 85.3 MB
  390. 17 734.2 KB
  391. 02_weekly-reading_RLbook2018.pdf 85.3 MB
  392. 18 734.2 KB
  393. 02_weekly-reading_RLbook2018.pdf 85.3 MB
  394. 19 734.2 KB
  395. 05_chapter-summary_RLbook2018.pdf 85.3 MB
  396. 20 734.2 KB
  397. 06_text-book-part-1-summary_RLbook2018.pdf 85.3 MB
  398. 21 734.2 KB
  399. 06_chapter-summary_RLbook2018.pdf 85.3 MB
  400. 22 734.2 KB
  401. 02_weekly-reading-on-policy-prediction-with-approximation_RLbook2018.pdf 85.3 MB
  402. 23 734.2 KB
  403. 02_michael-littman-the-reward-hypothesis.mp4 84.0 MB
  404. 24 1014.7 KB
  405. 03_andy-barto-and-rich-sutton-more-on-the-history-of-rl.mp4 80.2 MB
  406. 25 813.0 KB
  407. 03_doina-precup-building-knowledge-for-ai-agents-with-reinforcement-learning.mp4 55.3 MB
  408. 26 729.9 KB
  409. 05_rich-sutton-and-andy-barto-a-brief-history-of-rl.mp4 48.7 MB
  410. 27 259.0 KB
  411. 03_warren-powell-approximate-dynamic-programming-for-fleet-management-short.mp4 47.1 MB
  412. 28 895.8 KB
  413. 03_meet-your-instructors.mp4 43.9 MB
  414. 29 137.6 KB
  415. 02_meet-your-instructors.mp4 43.9 MB
  416. 30 137.6 KB
  417. 02_meet-your-instructors.mp4 43.9 MB
  418. 31 137.6 KB
  419. 02_meet-your-instructors.mp4 43.9 MB
  420. 32 137.6 KB
  421. 03_david-silver-on-deep-learning-rl-ai.mp4 41.4 MB
  422. 33 601.5 KB
  423. 05_csaba-szepesvari-on-problem-landscape.mp4 38.8 MB
  424. 34 198.0 KB
  425. 02_andy-barto-on-what-are-eligibility-traces-and-why-are-they-so-named.mp4 38.5 MB
  426. 35 503.7 KB
  427. 04_emma-brunskill-batch-reinforcement-learning.mp4 37.4 MB
  428. 36 629.9 KB
  429. 04_rich-sutton-the-importance-of-td-learning.mp4 35.6 MB
  430. 37 363.3 KB
  431. 03_drew-bagnell-self-driving-robotics-and-model-based-rl.mp4 35.2 MB
  432. 38 804.8 KB
  433. 06_andy-and-rich-advice-for-students.mp4 33.4 MB
  434. 39 625.2 KB
  435. 02_course-introduction.mp4 32.4 MB
  436. 40 622.0 KB
  437. 02_drew-bagnell-on-system-id-optimal-control.mp4 31.3 MB
  438. 41 730.9 KB
  439. 02_joelle-pineau-about-rl-that-matters.mp4 29.5 MB
  440. 42 515.0 KB
  441. 02_demonstration-with-actor-critic.mp4 28.8 MB
  442. 43 189.4 KB
  443. 03_susan-murphy-on-rl-in-mobile-health.mp4 27.6 MB
  444. 44 376.2 KB
  445. 02_satinder-singh-on-intrinsic-rewards.mp4 26.9 MB
  446. 45 90.4 KB
  447. 04_advantages-of-policy-parameterization.mp4 26.1 MB
  448. 46 966.6 KB
  449. 03_moving-to-parameterized-functions.mp4 24.4 MB
  450. 47 636.5 KB
  451. 05_martin-riedmiller-on-the-collect-and-infer-framework-for-data-efficient-rl.mp4 23.5 MB
  452. 48 471.8 KB
  453. 06_using-tile-coding-in-td.mp4 23.1 MB
  454. 49 952.7 KB
  455. 01_congratulations-course-4-preview.mp4 22.1 MB
  456. 50 908.7 KB
  457. 01_course-4-introduction.mp4 22.1 MB
  458. 51 908.7 KB
  459. 01_what-is-the-trade-off.mp4 21.6 MB
  460. 52 425.8 KB
  461. 04_meeting-with-martha-in-depth-on-experience-replay.mp4 21.4 MB
  462. 53 593.0 KB
  463. 04_value-functions.mp4 21.1 MB
  464. 54 925.0 KB
  465. 04_state-aggregation-with-monte-carlo.mp4 20.3 MB
  466. 55 762.6 KB
  467. 03_gaussian-policies-for-continuous-actions.mp4 20.0 MB
  468. 56 50.2 KB
  469. 02_estimating-action-values-incrementally.mp4 19.4 MB
  470. 57 616.2 KB
  471. 01_average-reward-a-new-way-of-formulating-control-problems.mp4 19.1 MB
  472. 58 945.5 KB
  473. 03_lets-review-average-reward-a-new-way-of-formulating-control-problems.mp4 19.1 MB
  474. 59 945.5 KB
  475. 04_iterative-policy-evaluation.mp4 18.8 MB
  476. 60 216.9 KB
  477. 03_specialization-wrap-up.mp4 18.6 MB
  478. 61 385.0 KB
  479. 01_optimal-policies.mp4 18.5 MB
  480. 62 548.4 KB
  481. 01_specialization-introduction.mp4 18.3 MB
  482. 63 760.1 KB
  483. 03_episodic-sarsa-with-function-approximation.mp4 18.1 MB
  484. 64 969.1 KB
  485. 04_generalization-properties-of-coarse-coding.mp4 18.0 MB
  486. 65 25.3 KB
  487. 02_policy-iteration.mp4 17.9 MB
  488. 66 138.6 KB
  489. 03_learning-policies-directly.mp4 17.1 MB
  490. 67 917.1 KB
  491. 01_bellman-equation-derivation.mp4 17.0 MB
  492. 68 994.2 KB
  493. 03_using-optimal-value-functions-to-get-optimal-policies.mp4 16.7 MB
  494. 69 275.1 KB
  495. 01_actor-critic-with-softmax-policies.mp4 16.5 MB
  496. 70 480.3 KB
  497. 01_course-3-introduction.mp4 16.3 MB
  498. 71 686.8 KB
  499. 03_week-1-summary.mp4 16.3 MB
  500. 72 701.7 KB
  501. 03_sequential-decision-making-with-evaluative-feedback.mp4 16.3 MB
  502. 73 742.8 KB
  503. 04_using-monte-carlo-for-prediction.mp4 16.2 MB
  504. 74 845.0 KB
  505. 01_agent-architecture-meeting-with-martha-overview-of-design-choices.mp4 15.6 MB
  506. 75 387.4 KB
  507. 01_gradient-descent-for-training-neural-networks.mp4 15.5 MB
  508. 76 477.0 KB
  509. 04_episodic-sarsa-in-mountain-car.mp4 15.5 MB
  510. 77 541.7 KB
  511. 01_semi-gradient-td-for-policy-evaluation.mp4 15.3 MB
  512. 78 666.0 KB
  513. 03_deep-neural-networks.mp4 15.3 MB
  514. 79 686.1 KB
  515. 03_gradient-monte-for-policy-evaluation.mp4 15.2 MB
  516. 80 781.5 KB
  517. 02_introducing-gradient-descent.mp4 15.1 MB
  518. 81 926.2 KB
  519. 03_specifying-policies.mp4 15.0 MB
  520. 82 12.4 KB
  521. 04_your-specialization-roadmap.mp4 14.9 MB
  522. 83 119.1 KB
  523. 03_what-is-monte-carlo.mp4 14.9 MB
  524. 84 123.3 KB
  525. 01_why-does-off-policy-learning-matter.mp4 14.4 MB
  526. 85 622.0 KB
  527. 01_lets-review-optimization-strategies-for-nns.mp4 14.3 MB
  528. 86 734.3 KB
  529. 02_optimization-strategies-for-nns.mp4 14.3 MB
  530. 87 734.3 KB
  531. 01_learning-action-values.mp4 14.2 MB
  532. 88 802.9 KB
  533. 02_actor-critic-algorithm.mp4 14.1 MB
  534. 89 949.5 KB
  535. 04_lets-review-actor-critic-algorithm.mp4 14.1 MB
  536. 90 949.5 KB
  537. 02_efficiency-of-dynamic-programming.mp4 14.0 MB
  538. 91 989.1 KB
  539. 03_solving-the-blackjack-example.mp4 13.9 MB
  540. 92 94.6 KB
  541. 02_the-true-objective-for-td.mp4 13.7 MB
  542. 93 351.8 KB
  543. 01_estimating-the-policy-gradient.mp4 13.6 MB
  544. 94 375.8 KB
  545. 01_the-objective-for-learning-policies.mp4 13.4 MB
  546. 95 660.6 KB
  547. 03_policy-evaluation-vs-control.mp4 13.3 MB
  548. 96 698.2 KB
  549. 01_initial-project-meeting-with-martha-formalizing-the-problem.mp4 13.3 MB
  550. 97 766.6 KB
  551. 02_optimistic-initial-values.mp4 13.1 MB
  552. 98 894.4 KB
  553. 04_generalization-and-discrimination.mp4 12.9 MB
  554. 99 144.1 KB
  555. 01_epsilon-soft-policies.mp4 12.7 MB
  556. 100 319.1 KB
  557. 01_continuing-tasks.mp4 12.7 MB
  558. 101 338.3 KB
  559. 01_meeting-with-adam-getting-the-agent-details-right.mp4 12.6 MB
  560. 102 410.3 KB
  561. 03_off-policy-monte-carlo-prediction.mp4 12.5 MB
  562. 103 496.3 KB
  563. 01_flexibility-of-the-policy-iteration-framework.mp4 12.4 MB
  564. 104 569.9 KB
  565. 03_markov-decision-processes.mp4 12.4 MB
  566. 105 659.1 KB
  567. 01_lets-review-markov-decision-processes.mp4 12.4 MB
  568. 106 659.1 KB
  569. 04_examples-of-mdps.mp4 12.2 MB
  570. 107 815.0 KB
  571. 04_week-3-summary.mp4 11.9 MB
  572. 108 55.3 KB
  573. 02_in-depth-with-changing-environments.mp4 11.9 MB
  574. 109 58.5 KB
  575. 04_jonathan-langford-contextual-bandits-for-real-world-reinforcement-learning.mp4 11.9 MB
  576. 110 65.8 KB
  577. 02_why-bellman-equations.mp4 11.9 MB
  578. 111 131.8 KB
  579. 03_upper-confidence-bound-ucb-action-selection.mp4 11.8 MB
  580. 112 234.1 KB
  581. 02_comparing-td-and-monte-carlo-with-state-aggregation.mp4 11.5 MB
  582. 113 466.2 KB
  583. 01_meeting-with-adam-parameter-studies-in-rl.mp4 11.5 MB
  584. 114 525.1 KB
  585. 03_what-is-a-model.mp4 11.3 MB
  586. 115 685.6 KB
  587. 01_course-introduction.mp4 11.3 MB
  588. 116 751.5 KB
  589. 02_the-dyna-algorithm.mp4 11.2 MB
  590. 117 780.5 KB
  591. 01_congratulations.mp4 11.2 MB
  592. 118 842.2 KB
  593. 01_exploration-under-function-approximation.mp4 11.0 MB
  594. 119 977.5 KB
  595. 01_meeting-with-martha-discussing-your-results.mp4 11.0 MB
  596. 120 48.0 KB
  597. 01_the-value-error-objective.mp4 10.9 MB
  598. 121 142.9 KB
  599. 03_lets-review-dyna-q-learning-in-a-simple-maze.mp4 10.8 MB
  600. 122 248.9 KB
  601. 03_dyna-q-learning-in-a-simple-maze.mp4 10.8 MB
  602. 123 248.9 KB
  603. 05_framing-value-estimation-as-supervised-learning.mp4 10.7 MB
  604. 124 315.9 KB
  605. 03_what-is-temporal-difference-td-learning.mp4 10.3 MB
  606. 125 709.2 KB
  607. 02_optimal-value-functions.mp4 10.2 MB
  608. 126 825.3 KB
  609. 01_policy-improvement.mp4 10.0 MB
  610. 127 8.2 KB
  611. 04_week-4-summary.mp4 10.0 MB
  612. 128 42.0 KB
  613. 03_how-is-q-learning-off-policy.mp4 10.0 MB
  614. 129 43.9 KB
  615. 01_the-linear-td-update.mp4 9.9 MB
  616. 130 102.2 KB
  617. 01_lets-review-comparing-td-and-monte-carlo.mp4 9.8 MB
  618. 131 192.9 KB
  619. 02_comparing-td-and-monte-carlo.mp4 9.8 MB
  620. 132 192.9 KB
  621. 05_week-4-summary.mp4 9.6 MB
  622. 133 396.8 KB
  623. 01_the-dyna-architecture.mp4 9.6 MB
  624. 134 415.3 KB
  625. 05_week-1-summary.mp4 9.6 MB
  626. 135 416.4 KB
  627. 03_coarse-coding.mp4 9.6 MB
  628. 136 422.8 KB
  629. 02_non-linear-approximation-with-neural-networks.mp4 9.6 MB
  630. 137 422.9 KB
  631. 01_lets-review-non-linear-approximation-with-neural-networks.mp4 9.6 MB
  632. 138 422.9 KB
  633. 05_week-1-summary.mp4 9.5 MB
  634. 139 534.7 KB
  635. 02_the-policy-gradient-theorem.mp4 9.3 MB
  636. 140 703.8 KB
  637. 02_lets-review-examples-of-episodic-and-continuing-tasks.mp4 9.1 MB
  638. 141 880.6 KB
  639. 02_examples-of-episodic-and-continuing-tasks.mp4 9.1 MB
  640. 142 880.6 KB
  641. 01_the-advantages-of-temporal-difference-learning.mp4 9.1 MB
  642. 143 926.6 KB
  643. 03_week-3-review.mp4 8.9 MB
  644. 144 125.9 KB
  645. 04_week-2-review.mp4 8.5 MB
  646. 145 513.3 KB
  647. 01_the-goal-of-reinforcement-learning.mp4 8.0 MB
  648. 146 1004.9 KB
  649. 01_meeting-with-niko-choosing-the-learning-algorithm.mp4 7.9 MB
  650. 147 122.9 KB
  651. 02_lets-review-what-is-q-learning.mp4 7.8 MB
  652. 148 166.6 KB
  653. 01_what-is-q-learning.mp4 7.8 MB
  654. 149 166.6 KB
  655. 01_random-tabular-q-planning.mp4 7.8 MB
  656. 150 177.8 KB
  657. 02_course-wrap-up.mp4 7.8 MB
  658. 151 246.6 KB
  659. 01_what-if-the-model-is-inaccurate.mp4 7.7 MB
  660. 152 315.9 KB
  661. 05_expected-sarsa-with-function-approximation.mp4 7.6 MB
  662. 153 379.7 KB
  663. 02_lets-review-expected-sarsa-with-function-approximation.mp4 7.6 MB
  664. 154 379.7 KB
  665. 05_tile-coding.mp4 7.6 MB
  666. 155 442.9 KB
  667. 02_importance-sampling.mp4 7.4 MB
  668. 156 602.7 KB
  669. 04_week-2-summary.mp4 7.4 MB
  670. 157 607.4 KB
  671. 03_sarsa-gpi-with-td.mp4 7.4 MB
  672. 158 633.7 KB
  673. 02_q-learning-in-the-windy-grid-world.mp4 7.2 MB
  674. 159 779.6 KB
  675. 01_what-is-a-neural-network.mp4 7.0 MB
  676. 160 997.8 KB
  677. 04_comparing-sample-and-distribution-models.mp4 6.6 MB
  678. 161 363.2 KB
  679. 01_using-monte-carlo-for-action-values.mp4 6.5 MB
  680. 162 544.5 KB
  681. 01_expected-sarsa.mp4 6.3 MB
  682. 163 752.7 KB
  683. 01_lets-review-expected-sarsa.mp4 6.3 MB
  684. 164 752.7 KB
  685. 04_sarsa-in-the-windy-grid-world.mp4 5.9 MB
  686. 165 152.1 KB
  687. 02_expected-sarsa-in-the-cliff-world.mp4 5.7 MB
  688. 166 318.6 KB
  689. 03_week-2-summary.mp4 5.4 MB
  690. 167 592.6 KB
  691. 03_generality-of-expected-sarsa.mp4 5.2 MB
  692. 168 805.6 KB
  693. 02_using-monte-carlo-methods-for-generalized-policy-iteration.mp4 5.2 MB
  694. 169 847.6 KB
  695. 01_congratulations.mp4 4.4 MB
  696. 170 658.2 KB
  697. 04_week-4-summary.mp4 4.3 MB
  698. 171 764.3 KB
  699. 04_week-3-summary.mp4 3.7 MB

Discussion