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Coursera-Practical-Reinf-1LRJt
001.Welcome\\/001. Why should you care.mp432.42MB- 001.Welcome\\/002. Reinforcement learning vs all.mp410.80MB
- 002.Reinforcement Learning\\/003. Multi-armed bandit.mp417.88MB
- 002.Reinforcement Learning\\/004. Decision process & applications.mp423.01MB
- 003.Black box optimization\\/005. rkov Decision Process.mp418.00MB
- 003.Black box optimization\\/006. Crossentropy method.mp436.01MB
- 003.Black box optimization\\/007. Approxite crossentropy method.mp419.27MB
- 003.Black box optimization\\/008. More on approxite crossentropy method.mp422.89MB
- 004.All the cool stuff that isn\t in the ba<x>se track\\/009. Evolution strategies core idea.mp420.86MB
- 004.All the cool stuff that isn\t in the ba<x>se track\\/010. Evolution strategies th problems.mp417.73MB
- 004.All the cool stuff that isn\t in the ba<x>se track\\/011. Evolution strategies log-derivative trick.mp427.84MB
- 004.All the cool stuff that isn\t in the ba<x>se track\\/012. Evolution strategies duct tape.mp421.17MB
- 004.All the cool stuff that isn\t in the ba<x>se track\\/013. Blackbox optimization drawbacks.mp415.21MB
- 005.Striving for reward\\/014. Reward design.mp449.70MB
- 006.Belln equations\\/015. State and Action Value Functions.mp437.31MB
- 006.Belln 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-ba<x>sed 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.mp410.30MB
- 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 ba<x>sed RL.mp433.76MB
- 011.Limitations of Tabular Methods\\/027. Difficulties with Approxite 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-ba<x>sed RL vs Value-ba<x>sed RL\\/034. Intuition.mp434.87MB
- 014.Policy-ba<x>sed RL vs Value-ba<x>sed RL\\/035. All Kinds of Policies.mp416.05MB
- 014.Policy-ba<x>sed RL vs Value-ba<x>sed RL\\/036. Policy gradient forli.mp431.56MB
- 014.Policy-ba<x>sed RL vs Value-ba<x>sed 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-ba<x>sed vs Value-ba<x>sed.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-ba<x>sed exploration\\/048. Intuitive explanation.mp422.26MB
- 018.Uncertainty-ba<x>sed exploration\\/049. Thompson Sampling.mp417.09MB
- 018.Uncertainty-ba<x>sed exploration\\/050. Optimi in face of uncertainty.mp416.54MB
- 018.Uncertainty-ba<x>sed exploration\\/051. UCB-1.mp422.19MB
- 018.Uncertainty-ba<x>sed exploration\\/052. Bayesian UCB.mp440.80MB
- 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
- CreateTime2022-09-28
- UpdateTime2022-10-01
- FileTotalCount111
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