[CourseClub.NET] Coursera - Practical Reinforcement Learning - 磁力狗

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文档列表

013.Honor/033. Partial observability.mp4 60.0 MB
019.Planning with Monte Carlo Tree Search/053. Introduction to planning.mp4 54.1 MB
011.Limitations of Tabular Methods/025. Supervised & Reinforcement Learning.mp4 53.1 MB
005.Striving for reward/014. Reward design.mp4 52.1 MB
011.Limitations of Tabular Methods/027. Difficulties with Approximate Methods.mp4 49.3 MB
018.Uncertainty-based exploration/052. Bayesian UCB.mp4 42.8 MB
009.On-policy vs off-policy/023. Accounting for exploration. Expected Value SARSA..mp4 39.6 MB
006.Bellman equations/015. State and Action Value Functions.mp4 39.1 MB
003.Black box optimization/006. Crossentropy method.mp4 37.8 MB
014.Policy-based RL vs Value-based RL/034. Intuition.mp4 36.6 MB
013.Honor/032. More DQN tricks.mp4 35.6 MB
011.Limitations of Tabular Methods/026. Loss functions in value based RL.mp4 35.4 MB
001.Welcome/001. Why should you care.mp4 34.0 MB
007.Generalized Policy Iteration/017. Policy evaluation & improvement.mp4 33.5 MB
014.Policy-based RL vs Value-based RL/036. Policy gradient formalism.mp4 33.1 MB
015.REINFORCE/038. REINFORCE.mp4 32.9 MB
019.Planning with Monte Carlo Tree Search/054. Monte Carlo Tree Search.mp4 32.4 MB
008.Model-free learning/020. Monte-Carlo & Temporal Difference; Q-learning.mp4 31.6 MB
012.Case Study Deep Q-Network/029. DQN the internals.mp4 31.1 MB
008.Model-free learning/019. Model-based vs model-free.mp4 30.2 MB
008.Model-free learning/021. Exploration vs Exploitation.mp4 29.6 MB
004.All the cool stuff that isn't in the base track/011. Evolution strategies log-derivative trick.mp4 29.2 MB
012.Case Study Deep Q-Network/028. DQN bird's eye view.mp4 29.1 MB
010.Experience Replay/024. On-policy vs off-policy; Experience replay.mp4 28.0 MB
016.Actor-critic/042. Case study A3C.mp4 27.4 MB
017.Measuting exploration/045. Recap bandits.mp4 25.9 MB
016.Actor-critic/039. Advantage actor-critic.mp4 25.8 MB
007.Generalized Policy Iteration/018. Policy and value iteration.mp4 25.3 MB
016.Actor-critic/044. Combining supervised & reinforcement learning.mp4 25.2 MB
002.Reinforcement Learning/004. Decision process & applications.mp4 24.1 MB
003.Black box optimization/008. More on approximate crossentropy method.mp4 24.0 MB
018.Uncertainty-based exploration/048. Intuitive explanation.mp4 23.3 MB
018.Uncertainty-based exploration/051. UCB-1.mp4 23.3 MB
017.Measuting exploration/046. Regret measuring the quality of exploration.mp4 22.3 MB
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/009. Evolution strategies core idea.mp4 21.9 MB
013.Honor/031. Double Q-learning.mp4 21.5 MB
003.Black box optimization/007. Approximate crossentropy method.mp4 20.2 MB
013.Honor/030. DQN statistical issues.mp4 20.2 MB
017.Measuting exploration/047. The message just repeats. 'Regret, Regret, Regret.'.mp4 19.3 MB
006.Bellman equations/016. Measuring Policy Optimality.mp4 19.0 MB
003.Black box optimization/005. Markov Decision Process.mp4 18.9 MB
002.Reinforcement Learning/003. Multi-armed bandit.mp4 18.7 MB
004.All the cool stuff that isn't in the base track/010. Evolution strategies math problems.mp4 18.6 MB
016.Actor-critic/040. Duct tape zone.mp4 18.4 MB
018.Uncertainty-based exploration/049. Thompson Sampling.mp4 17.9 MB
016.Actor-critic/041. Policy-based vs Value-based.mp4 17.6 MB
018.Uncertainty-based exploration/050. Optimism in face of uncertainty.mp4 17.3 MB
014.Policy-based RL vs Value-based RL/035. All Kinds of Policies.mp4 16.8 MB
004.All the cool stuff that isn't in the base track/013. Blackbox optimization drawbacks.mp4 16.0 MB
016.Actor-critic/043. A3C case study (2 2).mp4 15.7 MB
014.Policy-based RL vs Value-based RL/037. The log-derivative trick.mp4 13.9 MB
001.Welcome/002. Reinforcement learning vs all.mp4 11.3 MB
008.Model-free learning/022. Footnote Monte-Carlo vs Temporal Difference.mp4 10.8 MB
013.Honor/033. Partial observability.srt 28.4 kB
019.Planning with Monte Carlo Tree Search/053. Introduction to planning.srt 26.0 kB
011.Limitations of Tabular Methods/025. Supervised & Reinforcement Learning.srt 26.0 kB
005.Striving for reward/014. Reward design.srt 23.8 kB
011.Limitations of Tabular Methods/027. Difficulties with Approximate Methods.srt 22.4 kB
018.Uncertainty-based exploration/052. Bayesian UCB.srt 19.8 kB
006.Bellman equations/015. State and Action Value Functions.srt 18.7 kB
009.On-policy vs off-policy/023. Accounting for exploration. Expected Value SARSA..srt 17.7 kB
013.Honor/032. More DQN tricks.srt 16.7 kB
014.Policy-based RL vs Value-based RL/034. Intuition.srt 15.9 kB
003.Black box optimization/006. Crossentropy method.srt 15.9 kB
001.Welcome/001. Why should you care.srt 15.8 kB
011.Limitations of Tabular Methods/026. Loss functions in value based RL.srt 15.5 kB
019.Planning with Monte Carlo Tree Search/054. Monte Carlo Tree Search.srt 15.2 kB
008.Model-free learning/020. Monte-Carlo & Temporal Difference; Q-learning.srt 14.9 kB
007.Generalized Policy Iteration/017. Policy evaluation & improvement.srt 14.8 kB
008.Model-free learning/019. Model-based vs model-free.srt 14.4 kB
015.REINFORCE/038. REINFORCE.srt 14.3 kB
008.Model-free learning/021. Exploration vs Exploitation.srt 14.3 kB
014.Policy-based RL vs Value-based RL/036. Policy gradient formalism.srt 13.6 kB
004.All the cool stuff that isn't in the base track/011. Evolution strategies log-derivative trick.srt 12.9 kB
012.Case Study Deep Q-Network/029. DQN the internals.srt 12.5 kB
007.Generalized Policy Iteration/018. Policy and value iteration.srt 12.3 kB
017.Measuting exploration/045. Recap bandits.srt 12.2 kB
016.Actor-critic/044. Combining supervised & reinforcement learning.srt 12.2 kB
016.Actor-critic/039. Advantage actor-critic.srt 12.1 kB
012.Case Study Deep Q-Network/028. DQN bird's eye view.srt 11.7 kB
010.Experience Replay/024. On-policy vs off-policy; Experience replay.srt 11.5 kB
016.Actor-critic/042. Case study A3C.srt 11.4 kB
018.Uncertainty-based exploration/048. Intuitive explanation.srt 11.2 kB
003.Black box optimization/008. More on approximate crossentropy method.srt 10.7 kB
018.Uncertainty-based exploration/051. UCB-1.srt 10.6 kB
017.Measuting exploration/046. Regret measuring the quality of exploration.srt 10.4 kB
002.Reinforcement Learning/004. Decision process & applications.srt 9.9 kB
004.All the cool stuff that isn't in the base track/012. Evolution strategies duct tape.srt 9.9 kB
013.Honor/031. Double Q-learning.srt 9.7 kB
013.Honor/030. DQN statistical issues.srt 9.4 kB
017.Measuting exploration/047. The message just repeats. 'Regret, Regret, Regret.'.srt 8.9 kB
004.All the cool stuff that isn't in the base track/010. Evolution strategies math problems.srt 8.8 kB
006.Bellman equations/016. Measuring Policy Optimality.srt 8.7 kB
003.Black box optimization/005. Markov Decision Process.srt 8.5 kB
003.Black box optimization/007. Approximate crossentropy method.srt 8.4 kB
018.Uncertainty-based exploration/049. Thompson Sampling.srt 8.1 kB
018.Uncertainty-based exploration/050. Optimism in face of uncertainty.srt 8.1 kB
016.Actor-critic/040. Duct tape zone.srt 8.0 kB
014.Policy-based RL vs Value-based RL/035. All Kinds of Policies.srt 7.6 kB
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/013. Blackbox optimization drawbacks.srt 7.5 kB
002.Reinforcement Learning/003. Multi-armed bandit.srt 7.4 kB
016.Actor-critic/041. Policy-based vs Value-based.srt 7.2 kB
016.Actor-critic/043. A3C case study (2 2).srt 6.1 kB
014.Policy-based RL vs Value-based RL/037. The log-derivative trick.srt 6.0 kB
001.Welcome/002. Reinforcement learning vs all.srt 5.0 kB
008.Model-free learning/022. Footnote Monte-Carlo vs Temporal Difference.srt 4.9 kB
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