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CourseClub NET Coursera Practical Reinforcement Learning

Name

[CourseClub.NET] Coursera - Practical Reinforcement Learning

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Total Size

1.5 GB

Total Files

110

Hash

DA9C0F62F333DB6C8EC4225316AE6B96055AD1B3

/001.Welcome/

001. Why should you care.mp4

34.0 MB

001. Why should you care.srt

15.8 KB

002. Reinforcement learning vs all.mp4

11.3 MB

002. Reinforcement learning vs all.srt

5.0 KB

/002.Reinforcement Learning/

003. Multi-armed bandit.mp4

18.7 MB

003. Multi-armed bandit.srt

7.4 KB

004. Decision process & applications.mp4

24.1 MB

004. Decision process & applications.srt

9.9 KB

/003.Black box optimization/

005. Markov Decision Process.mp4

18.9 MB

005. Markov Decision Process.srt

8.5 KB

006. Crossentropy method.mp4

37.8 MB

006. Crossentropy method.srt

15.9 KB

007. Approximate crossentropy method.mp4

20.2 MB

007. Approximate crossentropy method.srt

8.4 KB

008. More on approximate crossentropy method.mp4

24.0 MB

008. More on approximate crossentropy method.srt

10.7 KB

/004.All the cool stuff that isn't in the base track/

009. Evolution strategies core idea.mp4

21.9 MB

009. Evolution strategies core idea.srt

7.5 KB

010. Evolution strategies math problems.mp4

18.6 MB

010. Evolution strategies math problems.srt

8.8 KB

011. Evolution strategies log-derivative trick.mp4

29.2 MB

011. Evolution strategies log-derivative trick.srt

12.9 KB

012. Evolution strategies duct tape.mp4

22.2 MB

012. Evolution strategies duct tape.srt

9.9 KB

013. Blackbox optimization drawbacks.mp4

16.0 MB

013. Blackbox optimization drawbacks.srt

7.5 KB

/005.Striving for reward/

014. Reward design.mp4

52.1 MB

014. Reward design.srt

23.8 KB

/006.Bellman equations/

015. State and Action Value Functions.mp4

39.1 MB

015. State and Action Value Functions.srt

18.7 KB

016. Measuring Policy Optimality.mp4

19.0 MB

016. Measuring Policy Optimality.srt

8.7 KB

/007.Generalized Policy Iteration/

017. Policy evaluation & improvement.mp4

33.5 MB

017. Policy evaluation & improvement.srt

14.8 KB

018. Policy and value iteration.mp4

25.3 MB

018. Policy and value iteration.srt

12.3 KB

/008.Model-free learning/

019. Model-based vs model-free.mp4

30.2 MB

019. Model-based vs model-free.srt

14.4 KB

020. Monte-Carlo & Temporal Difference; Q-learning.mp4

31.6 MB

020. Monte-Carlo & Temporal Difference; Q-learning.srt

14.9 KB

021. Exploration vs Exploitation.mp4

29.6 MB

021. Exploration vs Exploitation.srt

14.3 KB

022. Footnote Monte-Carlo vs Temporal Difference.mp4

10.8 MB

022. Footnote Monte-Carlo vs Temporal Difference.srt

4.9 KB

/009.On-policy vs off-policy/

023. Accounting for exploration. Expected Value SARSA..mp4

39.6 MB

023. Accounting for exploration. Expected Value SARSA..srt

17.7 KB

/010.Experience Replay/

024. On-policy vs off-policy; Experience replay.mp4

28.0 MB

024. On-policy vs off-policy; Experience replay.srt

11.5 KB

/011.Limitations of Tabular Methods/

025. Supervised & Reinforcement Learning.mp4

53.1 MB

025. Supervised & Reinforcement Learning.srt

26.0 KB

026. Loss functions in value based RL.mp4

35.4 MB

026. Loss functions in value based RL.srt

15.5 KB

027. Difficulties with Approximate Methods.mp4

49.3 MB

027. Difficulties with Approximate Methods.srt

22.4 KB

/012.Case Study Deep Q-Network/

028. DQN bird's eye view.mp4

29.1 MB

028. DQN bird's eye view.srt

11.7 KB

029. DQN the internals.mp4

31.1 MB

029. DQN the internals.srt

12.5 KB

/013.Honor/

030. DQN statistical issues.mp4

20.2 MB

030. DQN statistical issues.srt

9.4 KB

031. Double Q-learning.mp4

21.5 MB

031. Double Q-learning.srt

9.7 KB

032. More DQN tricks.mp4

35.6 MB

032. More DQN tricks.srt

16.7 KB

033. Partial observability.mp4

60.0 MB

033. Partial observability.srt

28.4 KB

/014.Policy-based RL vs Value-based RL/

034. Intuition.mp4

36.6 MB

034. Intuition.srt

15.9 KB

035. All Kinds of Policies.mp4

16.8 MB

035. All Kinds of Policies.srt

7.6 KB

036. Policy gradient formalism.mp4

33.1 MB

036. Policy gradient formalism.srt

13.6 KB

037. The log-derivative trick.mp4

13.9 MB

037. The log-derivative trick.srt

6.0 KB

/015.REINFORCE/

038. REINFORCE.mp4

32.9 MB

038. REINFORCE.srt

14.3 KB

/016.Actor-critic/

039. Advantage actor-critic.mp4

25.8 MB

039. Advantage actor-critic.srt

12.1 KB

040. Duct tape zone.mp4

18.4 MB

040. Duct tape zone.srt

8.0 KB

041. Policy-based vs Value-based.mp4

17.6 MB

041. Policy-based vs Value-based.srt

7.2 KB

042. Case study A3C.mp4

27.4 MB

042. Case study A3C.srt

11.4 KB

043. A3C case study (2 2).mp4

15.7 MB

043. A3C case study (2 2).srt

6.1 KB

044. Combining supervised & reinforcement learning.mp4

25.2 MB

044. Combining supervised & reinforcement learning.srt

12.2 KB

/017.Measuting exploration/

045. Recap bandits.mp4

25.9 MB

045. Recap bandits.srt

12.2 KB

046. Regret measuring the quality of exploration.mp4

22.3 MB

046. Regret measuring the quality of exploration.srt

10.4 KB

047. The message just repeats. 'Regret, Regret, Regret.'.mp4

19.3 MB

047. The message just repeats. 'Regret, Regret, Regret.'.srt

8.9 KB

/018.Uncertainty-based exploration/

048. Intuitive explanation.mp4

23.3 MB

048. Intuitive explanation.srt

11.2 KB

049. Thompson Sampling.mp4

17.9 MB

049. Thompson Sampling.srt

8.1 KB

050. Optimism in face of uncertainty.mp4

17.3 MB

050. Optimism in face of uncertainty.srt

8.1 KB

051. UCB-1.mp4

23.3 MB

051. UCB-1.srt

10.6 KB

052. Bayesian UCB.mp4

42.8 MB

052. Bayesian UCB.srt

19.8 KB

/019.Planning with Monte Carlo Tree Search/

053. Introduction to planning.mp4

54.1 MB

053. Introduction to planning.srt

26.0 KB

054. Monte Carlo Tree Search.mp4

32.4 MB

054. Monte Carlo Tree Search.srt

15.2 KB

/

[CourseClub.NET].url

0.1 KB

[DesireCourse.Com].url

0.1 KB

 

Total files 110


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