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种子名称： [FreeCoursesOnline.Me] Coursera - Practical Reinforcement Learning
文件类型： 视频
文件数目： 54个文件
文件大小： 1.41 GB
收录时间： 2019-5-12 10:09
已经下载： 3次
资源热度： 137
最近下载： 2024-12-15 18:27

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[FreeCoursesOnline.Me] Coursera - Practical Reinforcement Learning.torrent

001.Welcome/001. Why should you care.mp432.42MB

001.Welcome/002. Reinforcement learning vs all.mp410.8MB

002.Reinforcement Learning/003. Multi-armed bandit.mp417.88MB

002.Reinforcement Learning/004. Decision process & applications.mp423.01MB

003.Black box optimization/005. Markov Decision Process.mp418MB

003.Black box optimization/006. Crossentropy method.mp436.01MB

003.Black box optimization/007. Approximate crossentropy method.mp419.27MB

003.Black box optimization/008. More on approximate crossentropy method.mp422.89MB

004.All the cool stuff that isn't in the base track/009. Evolution strategies core idea.mp420.86MB

004.All the cool stuff that isn't in the base track/010. Evolution strategies math problems.mp417.73MB

004.All the cool stuff that isn't in the base track/011. Evolution strategies log-derivative trick.mp427.84MB

004.All the cool stuff that isn't in the base track/012. Evolution strategies duct tape.mp421.17MB

004.All the cool stuff that isn't in the base track/013. Blackbox optimization drawbacks.mp415.21MB

005.Striving for reward/014. Reward design.mp449.7MB

006.Bellman equations/015. State and Action Value Functions.mp437.31MB

006.Bellman equations/016. Measuring Policy Optimality.mp418.08MB

007.Generalized Policy Iteration/017. Policy evaluation & improvement.mp431.92MB

007.Generalized Policy Iteration/018. Policy and value iteration.mp424.16MB

008.Model-free learning/019. Model-based vs model-free.mp428.78MB

008.Model-free learning/020. Monte-Carlo & Temporal Difference; Q-learning.mp430.11MB

008.Model-free learning/021. Exploration vs Exploitation.mp428.23MB

008.Model-free learning/022. Footnote Monte-Carlo vs Temporal Difference.mp410.3MB

009.On-policy vs off-policy/023. Accounting for exploration. Expected Value SARSA..mp437.73MB

010.Experience Replay/024. On-policy vs off-policy; Experience replay.mp426.72MB

011.Limitations of Tabular Methods/025. Supervised & Reinforcement Learning.mp450.61MB

011.Limitations of Tabular Methods/026. Loss functions in value based RL.mp433.76MB

011.Limitations of Tabular Methods/027. Difficulties with Approximate Methods.mp447.03MB

012.Case Study Deep Q-Network/028. DQN bird's eye view.mp427.76MB

012.Case Study Deep Q-Network/029. DQN the internals.mp429.63MB

013.Honor/030. DQN statistical issues.mp419.22MB

013.Honor/031. Double Q-learning.mp420.46MB

013.Honor/032. More DQN tricks.mp433.94MB

013.Honor/033. Partial observability.mp457.23MB

014.Policy-based RL vs Value-based RL/034. Intuition.mp434.87MB

014.Policy-based RL vs Value-based RL/035. All Kinds of Policies.mp416.05MB

014.Policy-based RL vs Value-based RL/036. Policy gradient formalism.mp431.56MB

014.Policy-based RL vs Value-based RL/037. The log-derivative trick.mp413.29MB

015.REINFORCE/038. REINFORCE.mp431.42MB

016.Actor-critic/039. Advantage actor-critic.mp424.63MB

016.Actor-critic/040. Duct tape zone.mp417.53MB

016.Actor-critic/041. Policy-based vs Value-based.mp416.79MB

016.Actor-critic/042. Case study A3C.mp426.09MB

016.Actor-critic/043. A3C case study (2 2).mp414.96MB

016.Actor-critic/044. Combining supervised & reinforcement learning.mp424.02MB

017.Measuting exploration/045. Recap bandits.mp424.66MB

017.Measuting exploration/046. Regret measuring the quality of exploration.mp421.27MB

017.Measuting exploration/047. The message just repeats. 'Regret, Regret, Regret.'.mp418.43MB

018.Uncertainty-based exploration/048. Intuitive explanation.mp422.26MB

018.Uncertainty-based exploration/049. Thompson Sampling.mp417.09MB

018.Uncertainty-based exploration/050. Optimism in face of uncertainty.mp416.54MB

018.Uncertainty-based exploration/051. UCB-1.mp422.19MB

018.Uncertainty-based exploration/052. Bayesian UCB.mp440.8MB

019.Planning with Monte Carlo Tree Search/053. Introduction to planning.mp451.63MB

019.Planning with Monte Carlo Tree Search/054. Monte Carlo Tree Search.mp430.92MB