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[FreeCoursesOnline.Me] Coursera - Practical Reinforcement Learning
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[FreeCoursesOnline.Me] Coursera - Practical Reinforcement Learning
磁力链接/BT种子简介
种子哈希:
31b47a1285df93a33f1c80a563fd43b322fc434d
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1.41G
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4899
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极快
收录时间:
2018-09-25
最近下载:
2025-09-27
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主播 19
文件列表
001.Welcome/001. Why should you care.mp4
34.0 MB
001.Welcome/001. Why should you care.srt
15.8 kB
001.Welcome/002. Reinforcement learning vs all.mp4
11.3 MB
001.Welcome/002. Reinforcement learning vs all.srt
5.0 kB
002.Reinforcement Learning/003. Multi-armed bandit.mp4
18.7 MB
002.Reinforcement Learning/003. Multi-armed bandit.srt
7.4 kB
002.Reinforcement Learning/004. Decision process & applications.mp4
24.1 MB
002.Reinforcement Learning/004. Decision process & applications.srt
9.9 kB
003.Black box optimization/005. Markov Decision Process.mp4
18.9 MB
003.Black box optimization/005. Markov Decision Process.srt
8.5 kB
003.Black box optimization/006. Crossentropy method.mp4
37.8 MB
003.Black box optimization/006. Crossentropy method.srt
15.9 kB
003.Black box optimization/007. Approximate crossentropy method.mp4
20.2 MB
003.Black box optimization/007. Approximate crossentropy method.srt
8.4 kB
003.Black box optimization/008. More on approximate crossentropy method.mp4
24.0 MB
003.Black box optimization/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
004.All the cool stuff that isn't in the base track/009. Evolution strategies core idea.srt
7.5 kB
004.All the cool stuff that isn't in the base track/010. Evolution strategies math problems.mp4
18.6 MB
004.All the cool stuff that isn't in the base track/010. Evolution strategies math problems.srt
8.8 kB
004.All the cool stuff that isn't in the base track/011. Evolution strategies log-derivative trick.mp4
29.2 MB
004.All the cool stuff that isn't in the base track/011. Evolution strategies log-derivative trick.srt
12.9 kB
004.All the cool stuff that isn't in the base track/012. Evolution strategies duct tape.mp4
22.2 MB
004.All the cool stuff that isn't in the base track/012. Evolution strategies duct tape.srt
9.9 kB
004.All the cool stuff that isn't in the base track/013. Blackbox optimization drawbacks.mp4
16.0 MB
004.All the cool stuff that isn't in the base track/013. Blackbox optimization drawbacks.srt
7.5 kB
005.Striving for reward/014. Reward design.mp4
52.1 MB
005.Striving for reward/014. Reward design.srt
23.8 kB
006.Bellman equations/015. State and Action Value Functions.mp4
39.1 MB
006.Bellman equations/015. State and Action Value Functions.srt
18.7 kB
006.Bellman equations/016. Measuring Policy Optimality.mp4
19.0 MB
006.Bellman equations/016. Measuring Policy Optimality.srt
8.7 kB
007.Generalized Policy Iteration/017. Policy evaluation & improvement.mp4
33.5 MB
007.Generalized Policy Iteration/017. Policy evaluation & improvement.srt
14.8 kB
007.Generalized Policy Iteration/018. Policy and value iteration.mp4
25.3 MB
007.Generalized Policy Iteration/018. Policy and value iteration.srt
12.3 kB
008.Model-free learning/019. Model-based vs model-free.mp4
30.2 MB
008.Model-free learning/019. Model-based vs model-free.srt
14.4 kB
008.Model-free learning/020. Monte-Carlo & Temporal Difference; Q-learning.mp4
31.6 MB
008.Model-free learning/020. Monte-Carlo & Temporal Difference; Q-learning.srt
14.9 kB
008.Model-free learning/021. Exploration vs Exploitation.mp4
29.6 MB
008.Model-free learning/021. Exploration vs Exploitation.srt
14.3 kB
008.Model-free learning/022. Footnote Monte-Carlo vs Temporal Difference.mp4
10.8 MB
008.Model-free learning/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
009.On-policy vs off-policy/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
010.Experience Replay/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
011.Limitations of Tabular Methods/025. Supervised & Reinforcement Learning.srt
26.0 kB
011.Limitations of Tabular Methods/026. Loss functions in value based RL.mp4
35.4 MB
011.Limitations of Tabular Methods/026. Loss functions in value based RL.srt
15.5 kB
011.Limitations of Tabular Methods/027. Difficulties with Approximate Methods.mp4
49.3 MB
011.Limitations of Tabular Methods/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
012.Case Study Deep Q-Network/028. DQN bird's eye view.srt
11.7 kB
012.Case Study Deep Q-Network/029. DQN the internals.mp4
31.1 MB
012.Case Study Deep Q-Network/029. DQN the internals.srt
12.5 kB
013.Honor/030. DQN statistical issues.mp4
20.2 MB
013.Honor/030. DQN statistical issues.srt
9.4 kB
013.Honor/031. Double Q-learning.mp4
21.5 MB
013.Honor/031. Double Q-learning.srt
9.7 kB
013.Honor/032. More DQN tricks.mp4
35.6 MB
013.Honor/032. More DQN tricks.srt
16.7 kB
013.Honor/033. Partial observability.mp4
60.0 MB
013.Honor/033. Partial observability.srt
28.4 kB
014.Policy-based RL vs Value-based RL/034. Intuition.mp4
36.6 MB
014.Policy-based RL vs Value-based RL/034. Intuition.srt
15.9 kB
014.Policy-based RL vs Value-based RL/035. All Kinds of Policies.mp4
16.8 MB
014.Policy-based RL vs Value-based RL/035. All Kinds of Policies.srt
7.6 kB
014.Policy-based RL vs Value-based RL/036. Policy gradient formalism.mp4
33.1 MB
014.Policy-based RL vs Value-based RL/036. Policy gradient formalism.srt
13.6 kB
014.Policy-based RL vs Value-based RL/037. The log-derivative trick.mp4
13.9 MB
014.Policy-based RL vs Value-based RL/037. The log-derivative trick.srt
6.0 kB
015.REINFORCE/038. REINFORCE.mp4
32.9 MB
015.REINFORCE/038. REINFORCE.srt
14.3 kB
016.Actor-critic/039. Advantage actor-critic.mp4
25.8 MB
016.Actor-critic/039. Advantage actor-critic.srt
12.1 kB
016.Actor-critic/040. Duct tape zone.mp4
18.4 MB
016.Actor-critic/040. Duct tape zone.srt
8.0 kB
016.Actor-critic/041. Policy-based vs Value-based.mp4
17.6 MB
016.Actor-critic/041. Policy-based vs Value-based.srt
7.2 kB
016.Actor-critic/042. Case study A3C.mp4
27.4 MB
016.Actor-critic/042. Case study A3C.srt
11.4 kB
016.Actor-critic/043. A3C case study (2 2).mp4
15.7 MB
016.Actor-critic/043. A3C case study (2 2).srt
6.1 kB
016.Actor-critic/044. Combining supervised & reinforcement learning.mp4
25.2 MB
016.Actor-critic/044. Combining supervised & reinforcement learning.srt
12.2 kB
017.Measuting exploration/045. Recap bandits.mp4
25.9 MB
017.Measuting exploration/045. Recap bandits.srt
12.2 kB
017.Measuting exploration/046. Regret measuring the quality of exploration.mp4
22.3 MB
017.Measuting exploration/046. Regret measuring the quality of exploration.srt
10.4 kB
017.Measuting exploration/047. The message just repeats. 'Regret, Regret, Regret.'.mp4
19.3 MB
017.Measuting exploration/047. The message just repeats. 'Regret, Regret, Regret.'.srt
8.9 kB
018.Uncertainty-based exploration/048. Intuitive explanation.mp4
23.3 MB
018.Uncertainty-based exploration/048. Intuitive explanation.srt
11.2 kB
018.Uncertainty-based exploration/049. Thompson Sampling.mp4
17.9 MB
018.Uncertainty-based exploration/049. Thompson Sampling.srt
8.1 kB
018.Uncertainty-based exploration/050. Optimism in face of uncertainty.mp4
17.3 MB
018.Uncertainty-based exploration/050. Optimism in face of uncertainty.srt
8.1 kB
018.Uncertainty-based exploration/051. UCB-1.mp4
23.3 MB
018.Uncertainty-based exploration/051. UCB-1.srt
10.6 kB
018.Uncertainty-based exploration/052. Bayesian UCB.mp4
42.8 MB
018.Uncertainty-based exploration/052. Bayesian UCB.srt
19.8 kB
019.Planning with Monte Carlo Tree Search/053. Introduction to planning.mp4
54.1 MB
019.Planning with Monte Carlo Tree Search/053. Introduction to planning.srt
26.0 kB
019.Planning with Monte Carlo Tree Search/054. Monte Carlo Tree Search.mp4
32.4 MB
019.Planning with Monte Carlo Tree Search/054. Monte Carlo Tree Search.srt
15.2 kB
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133 Bytes
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119 Bytes
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252 Bytes
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