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Title: PRACTICAL REINFORCEMENT LEARNING
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[FreeCoursesOnline.Me] Coursera - Practical Reinforcement Learning 001.Welcome
  • 001. Why should you care.mp4 (32.4 MB)
  • 001. Why should you care.srt (15.4 KB)
  • 002. Reinforcement learning vs all.mp4 (10.8 MB)
  • 002. Reinforcement learning vs all.srt (4.9 KB)
002.Reinforcement Learning
  • 003. Multi-armed bandit.mp4 (17.9 MB)
  • 003. Multi-armed bandit.srt (7.3 KB)
  • 004. Decision process & applications.mp4 (23.0 MB)
  • 004. Decision process & applications.srt (9.7 KB)
003.Black box optimization
  • 005. Markov Decision Process.mp4 (18.0 MB)
  • 005. Markov Decision Process.srt (8.3 KB)
  • 006. Crossentropy method.mp4 (36.0 MB)
  • 006. Crossentropy method.srt (15.5 KB)
  • 007. Approximate crossentropy method.mp4 (19.3 MB)
  • 007. Approximate crossentropy method.srt (8.2 KB)
  • 008. More on approximate crossentropy method.mp4 (22.9 MB)
  • 008. More on approximate crossentropy method.srt (10.5 KB)
004.All the cool stuff that isn't in the base track
  • 009. Evolution strategies core idea.mp4 (20.9 MB)
  • 009. Evolution strategies core idea.srt (7.3 KB)
  • 010. Evolution strategies math problems.mp4 (17.7 MB)
  • 010. Evolution strategies math problems.srt (8.6 KB)
  • 011. Evolution strategies log-derivative trick.mp4 (27.8 MB)
  • 011. Evolution strategies log-derivative trick.srt (12.6 KB)
  • 012. Evolution strategies duct tape.mp4 (21.2 MB)
  • 012. Evolution strategies duct tape.srt (9.7 KB)
  • 013. Blackbox optimization drawbacks.mp4 (15.2 MB)
  • 013. Blackbox optimization drawbacks.srt (7.3 KB)
005.Striving for reward
  • 014. Reward design.mp4 (49.7 MB)
  • 014. Reward design.srt (23.2 KB)
006.Bellman equations
  • 015. State and Action Value Functions.mp4 (37.3 MB)
  • 015. State and Action Value Functions.srt (18.2 KB)
  • 016. Measuring Policy Optimality.mp4 (18.1 MB)
  • 016. Measuring Policy Optimality.srt (8.5 KB)
007.Generalized Policy Iteration
  • 017. Policy evaluation & improvement.mp4 (31.9 MB)
  • 017. Policy evaluation & improvement.srt (14.5 KB)
  • 018. Policy and value iteration.mp4 (24.2 MB)
  • 018. Policy and value iteration.srt (12.1 KB)
008.Model-free learning
  • 019. Model-based vs model-free.mp4 (28.8 MB)
  • 019. Model-based vs model-free.srt (14.1 KB)
  • 020. Monte-Carlo & Temporal Difference; Q-learning.mp4 (30.1 MB)
  • 020. Monte-Carlo & Temporal Difference; Q-learning.srt (14.5 KB)
  • 021. Exploration vs Exploitation.mp4 (28.2 MB)
  • 021. Exploration vs Exploitation.srt (14.0 KB)
  • 022. Footnote Monte-Carlo vs Temporal Difference.mp4 (10.3 MB)
  • 022. Footnote Monte-Carlo vs Temporal Difference.srt (4.8 KB)
009.On-policy vs off-policy
  • 023. Accounting for exploration. Expected Value SARSA..mp4 (37.7 MB)
  • 023. Accounting for exploration. Expected Value SARSA..srt (17.3 KB)
010.Experience Replay
  • 024. On-policy vs off-policy; Experience replay.mp4 (26.7 MB)
  • 024. On-policy vs off-policy; Experience replay.srt (11.2 KB)
011.Limitations of Tabular Methods
  • 025. Supervised & Reinforcement Learning.mp4 (50.6 MB)
  • 025. Supervised & Reinforcement Learning.srt (25.4 KB)
  • 026. Loss functions in value based RL.mp4 (33.8 MB)
  • 026. Loss functions in value based RL.srt (15.2 KB)
  • 027. Difficulties with Approximate Methods.mp4 (47.0 MB)
  • 027. Difficulties with Approximate Methods.srt (21.9 KB)
012.Case Study Deep Q-Network
  • 028. DQN bird's eye view.mp4 (27.8 MB)
  • 028. DQN bird's eye view.srt (11.4 KB)
  • 029. DQN the internals.mp4 (29.6 MB)
  • 029. DQN the internals.srt (12.3 KB)
013.Honor
  • 030. DQN statistical issues.mp4 (19.2 MB)
  • 030. DQN statistical issues.srt (9.2 KB)
  • 031. Double Q-learning.mp4 (20.5 MB)
  • 031. Double Q-learning.srt (9.4 KB)
  • 032. More DQN tricks.mp4 (33.9 MB)
  • 032. More DQN tricks.srt (16.4 KB)
  • 033. Partial observability.mp4 (57.2 MB)
  • 033. Partial observability.srt (27.7 KB)
014.Policy-based RL vs Value-based RL
  • 034. Intuition.mp4 (34.9 MB)
  • 034. Intuition.srt (15.6 KB)
  • 035. All Kinds of Policies.mp4 (16.0 MB)
  • 035. All Kinds of Policies.srt (7.4 KB)
  • 036. Policy gradient formalism.mp4 (31.6 MB)
  • 036. Policy gradient formalism.srt (13.3 KB)
  • 037. The log-derivative trick.mp4 (13.3 MB)
  • 037. The log-derivative trick.srt (5.9 KB)
015.REINFORCE
  • 038. REINFORCE.mp4 (31.4 MB)
  • 038. REINFORCE.srt (14.0 KB)
016.Actor-critic
  • 039. Advantage actor-critic.mp4 (24.6 MB)
  • 039. Advantage actor-critic.srt (11.8 KB)
  • 040. Duct tape zone.mp4 (17.5 MB)
  • 040. Duct tape zone.srt (7.8 KB)
  • 041. Policy-based vs Value-based.mp4 (16.8 MB)
  • 041. Policy-based vs Value-based.srt (7.1 KB)
  • 042. Case study A3C.mp4 (26.1 MB)
  • 042. Case study A3C.srt (11.1 KB)
  • 043. A3C case study (2 2).mp4 (15.0 MB)
  • 043. A3C case study (2 2).srt (6.0 KB)
  • 044. Combining supervised & reinforcement learning.mp4 (24.0 MB)
  • 044. Combining supervised & reinforcement learning.srt (11.9 KB)
017.Measuting exploration
  • 045. Recap bandits.mp4 (24.7 MB)
  • 045. Recap bandits.srt (11.9 KB)
  • 046. Regret measuring the quality of exploration.mp4 (21.3 MB)
  • 046. Regret measuring the quality of exploration.srt (10.2 KB)
  • 047. The message just repeats. 'Regret, Regret, Regret.'.mp4 (18.4 MB)
  • 047. The message just repeats. 'Regret, Regret, Regret.'.srt (8.7 KB)
018.Uncertainty-based exploration
  • 048. Intuitive explanation.mp4 (22.3 MB)
  • 048. Intuitive explanation.srt (10.9 KB)
  • 049. Thompson Sampling.mp4 (17.1 MB)
  • 049. Thompson Sampling.srt (7.9 KB)
  • 050. Optimism in face of uncertainty.mp4 (16.5 MB)
  • 050. Optimism in face of uncertainty.srt (7.9 KB)
  • 051. UCB-1.mp4 (22.2 MB)
  • 051. UCB-1.srt (10.4 KB)
  • 052. Bayesian UCB.mp4 (40.8 MB)
  • 052. Bayesian UCB.srt (19.3 KB)
019.Planning with Monte Carlo Tree Search
  • 053. Introduction to planning.mp4 (51.6 MB)
  • 053. Introduction to planning.srt (25.4 KB)
  • 054. Monte Carlo Tree Search.mp4 (30.9 MB)
  • 054. Monte Carlo Tree Search.srt (14.8 KB)
  • [FreeCoursesOnline.Me].url (0.1 KB)
  • [FreeTutorials.Us].url (0.1 KB)
  • [FTU Forum].url (0.2 KB)

  • Info

    It's gonna be fun! Do you have technical problems? Write to us: [email protected]

    Welcome to the Reinforcement Learning course. It's gonna be fun! Do you have technical problems? Write to us: [email protected] Curso 4 de 7 en. Programa Especializado - Aprendizaje automático avanzado.

    Quiz & Assignment of Coursera. Assignments will have bonus sections if you want to dig deeper. Everything essential to solving reinforcement learning problems is worth mentioning. We won't shun away from covering tricks and heuristics. For every major idea there should be a lab that allows to feel it on a practical problem.

    Practical Reinforcement Learning. Higher School of Economics via Coursera. Welcome to the Reinforcement Learning course. Here you will find out about: - foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. -- with math & batteries included. using deep neural networks for RL tasks -- also known as "the hype train". state of the art RL algorithms -- and how to apply duct tape to them for practical problems. and, of course, teaching your neural network to play games -- because that's what everyone thinks RL is about. We'll also use it for seq2seq and contextual bandits.

    Reinforcement learning is a subfield of AI/statistics focused on ing complicated environments and learning how to optimally. This is for any reinforcement learning related work ranging from purely computational RL in artificial intelligence to the models of RL in neuroscience. The standard introduction to RL is Sutton & Barto's Reinforcement Learning. Related subreddits: /r/machinelearning/.

    Practical Reinforcement Learning (Coursera). 4. Introduction to Reinforcement Learning. This course was lectured by prof. David Silver at the UCL (London’s Global University). This course is part of the Advanced Machine Learning Specialization, offered by the Russia’s Higher School of Economics. It’s a 4 week course which covers topics like value/policy iteration, q-learning, policy gradient and advances to deep reinforcement learning with Deep Q-Learning. The course covers topics like Markov Decision Process, Dynamic Programming, Value Function Approximation, Policy Gradient, Exploit-Exploration Dilemma and others. Start Date: 09/08/2019. Course Type: Common Course. Join Coursera today to learn data science, programming, business strategy, and more. Here you will find out about: - foundations of RL m. Course Tag. By Lord Voldemort Last updated Feb 6, 2019. About this course: Welcome to the Reinforcement Learning course. Here you will find out about: – foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. - with math & batteries included – using deep neural networks for RL tasks - also known as the hype train – state of the art RL algorithms - and how to apply duct tape to them for practical problems

    Coursera has the most reputable online training in Machine Learning (from Stanford U, by Andrew Ng), a fantastic Deep .

    Coursera has the most reputable online training in Machine Learning (from Stanford U, by Andrew Ng), a fantastic Deep Learning specialization (from deep. But it has very little offering in Reinforcement Learning, where Coursera clearly lags competition, even though it is hard to find quality online courses for a non-ridiculous price elsewhere. David Silver's recorded lectures at UCL are first class, as is Sutton and Barto's fantastic book, but this is not the same as a coursework with lessons, quizzes, exercises, assignments and perhaps a certificate in the end.

    Coursera Practical Reinforcement Learning. We'll also learn one simple algorithm that can solve reinforcement learning problems with embarrassing efficiency. Tags: machine learning. At the heart of RL: Dynamic Programming This week we'll consider the reinforcement learning formalisms in a more rigorous, mathematical way. You'll learn how to effectively compute the return your agent gets for a particular action - and how to pick best actions based on that return.

    [COURSERA] PRACTICAL REINFORCEMENT LEARNING [FCO]
    About this course: Welcome to the Reinforcement Learning course. Here you will find out about: – foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. — with math & batteries included – using deep neural networks for RL tasks — also known as “the hype train” – state of the art RL algorithms — and how to apply duct tape to them for practical problems. – and, of course, teaching your neural network to play games — because that’s what everyone thinks RL is about. We’ll also use it for seq2seq and contextual bandits. Jump in. It’s gonna be fun!
    For more Coursera and other Courses >>> https://www.freecoursesonline.me/
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PRACTICAL REINFORCEMENT LEARNING COURSERA