[COURSERA] PRACTICAL REINFORCEMENT LEARNING [FCO]
- CategoryOther
- TypeTutorials
- LanguageEnglish
- Total size1.4 GB
- Uploaded BySunRiseZone
- Downloads64
- Last checkedSep. 24th '18
- Date uploadedSep. 23rd '18
- Seeders 17
- Leechers10
[COURSERA] PRACTICAL REINFORCEMENT LEARNING [FCO]
About this course: Welcome to the Reinforcement Learning course. Here you will find out about: – foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. — with math & batteries included – using deep neural networks for RL tasks — also known as “the hype train” – state of the art RL algorithms — and how to apply duct tape to them for practical problems. – and, of course, teaching your neural network to play games — because that’s what everyone thinks RL is about. We’ll also use it for seq2seq and contextual bandits. Jump in. It’s gonna be fun!
For more Coursera and other Courses >>> https://www.freecoursesonline.me/
For More Udemy Free Courses >>> http://www.freetutorials.us
Files:
[FreeCoursesOnline.Me] Coursera - Practical Reinforcement Learning 001.Welcome- 001. Why should you care.mp4 (32.4 MB)
- 001. Why should you care.srt (15.4 KB)
- 002. Reinforcement learning vs all.mp4 (10.8 MB)
- 002. Reinforcement learning vs all.srt (4.9 KB)
- 003. Multi-armed bandit.mp4 (17.9 MB)
- 003. Multi-armed bandit.srt (7.3 KB)
- 004. Decision process & applications.mp4 (23.0 MB)
- 004. Decision process & applications.srt (9.7 KB)
- 005. Markov Decision Process.mp4 (18.0 MB)
- 005. Markov Decision Process.srt (8.3 KB)
- 006. Crossentropy method.mp4 (36.0 MB)
- 006. Crossentropy method.srt (15.5 KB)
- 007. Approximate crossentropy method.mp4 (19.3 MB)
- 007. Approximate crossentropy method.srt (8.2 KB)
- 008. More on approximate crossentropy method.mp4 (22.9 MB)
- 008. More on approximate crossentropy method.srt (10.5 KB)
- 009. Evolution strategies core idea.mp4 (20.9 MB)
- 009. Evolution strategies core idea.srt (7.3 KB)
- 010. Evolution strategies math problems.mp4 (17.7 MB)
- 010. Evolution strategies math problems.srt (8.6 KB)
- 011. Evolution strategies log-derivative trick.mp4 (27.8 MB)
- 011. Evolution strategies log-derivative trick.srt (12.6 KB)
- 012. Evolution strategies duct tape.mp4 (21.2 MB)
- 012. Evolution strategies duct tape.srt (9.7 KB)
- 013. Blackbox optimization drawbacks.mp4 (15.2 MB)
- 013. Blackbox optimization drawbacks.srt (7.3 KB)
- 014. Reward design.mp4 (49.7 MB)
- 014. Reward design.srt (23.2 KB)
- 015. State and Action Value Functions.mp4 (37.3 MB)
- 015. State and Action Value Functions.srt (18.2 KB)
- 016. Measuring Policy Optimality.mp4 (18.1 MB)
- 016. Measuring Policy Optimality.srt (8.5 KB)
- 017. Policy evaluation & improvement.mp4 (31.9 MB)
- 017. Policy evaluation & improvement.srt (14.5 KB)
- 018. Policy and value iteration.mp4 (24.2 MB)
- 018. Policy and value iteration.srt (12.1 KB)
- 019. Model-based vs model-free.mp4 (28.8 MB)
- 019. Model-based vs model-free.srt (14.1 KB)
- 020. Monte-Carlo & Temporal Difference; Q-learning.mp4 (30.1 MB)
- 020. Monte-Carlo & Temporal Difference; Q-learning.srt (14.5 KB)
- 021. Exploration vs Exploitation.mp4 (28.2 MB)
- 021. Exploration vs Exploitation.srt (14.0 KB)
- 022. Footnote Monte-Carlo vs Temporal Difference.mp4 (10.3 MB)
- 022. Footnote Monte-Carlo vs Temporal Difference.srt (4.8 KB)
- 023. Accounting for exploration. Expected Value SARSA..mp4 (37.7 MB)
- 023. Accounting for exploration. Expected Value SARSA..srt (17.3 KB)
- 024. On-policy vs off-policy; Experience replay.mp4 (26.7 MB)
- 024. On-policy vs off-policy; Experience replay.srt (11.2 KB)
- 025. Supervised & Reinforcement Learning.mp4 (50.6 MB)
- 025. Supervised & Reinforcement Learning.srt (25.4 KB)
- 026. Loss functions in value based RL.mp4 (33.8 MB)
- 026. Loss functions in value based RL.srt (15.2 KB)
- 027. Difficulties with Approximate Methods.mp4 (47.0 MB)
- 027. Difficulties with Approximate Methods.srt (21.9 KB)
- 028. DQN bird's eye view.mp4 (27.8 MB)
- 028. DQN bird's eye view.srt (11.4 KB)
- 029. DQN the internals.mp4 (29.6 MB)
- 029. DQN the internals.srt (12.3 KB)
- 030. DQN statistical issues.mp4 (19.2 MB)
- 030. DQN statistical issues.srt (9.2 KB)
- 031. Double Q-learning.mp4 (20.5 MB)
- 031. Double Q-learning.srt (9.4 KB)
- 032. More DQN tricks.mp4 (33.9 MB)
- 032. More DQN tricks.srt (16.4 KB)
- 033. Partial observability.mp4 (57.2 MB)
- 033. Partial observability.srt (27.7 KB)
- 034. Intuition.mp4 (34.9 MB)
- 034. Intuition.srt (15.6 KB)
- 035. All Kinds of Policies.mp4 (16.0 MB)
- 035. All Kinds of Policies.srt (7.4 KB)
- 036. Policy gradient formalism.mp4 (31.6 MB)
- 036. Policy gradient formalism.srt (13.3 KB)
- 037. The log-derivative trick.mp4 (13.3 MB)
- 037. The log-derivative trick.srt (5.9 KB)
- 038. REINFORCE.mp4 (31.4 MB)
- 038. REINFORCE.srt (14.0 KB)
- 039. Advantage actor-critic.mp4 (24.6 MB)
- 039. Advantage actor-critic.srt (11.8 KB)
- 040. Duct tape zone.mp4 (17.5 MB)
- 040. Duct tape zone.srt (7.8 KB)
- 041. Policy-based vs Value-based.mp4 (16.8 MB)
- 041. Policy-based vs Value-based.srt (7.1 KB)
- 042. Case study A3C.mp4 (26.1 MB)
- 042. Case study A3C.srt (11.1 KB)
- 043. A3C case study (2 2).mp4 (15.0 MB)
- 043. A3C case study (2 2).srt (6.0 KB)
- 044. Combining supervised & reinforcement learning.mp4 (24.0 MB)
- 044. Combining supervised & reinforcement learning.srt (11.9 KB)
- 045. Recap bandits.mp4 (24.7 MB)
- 045. Recap bandits.srt (11.9 KB)
- 046. Regret measuring the quality of exploration.mp4 (21.3 MB)
- 046. Regret measuring the quality of exploration.srt (10.2 KB)
- 047. The message just repeats. 'Regret, Regret, Regret.'.mp4 (18.4 MB)
- 047. The message just repeats.
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