[FreeCoursesOnline.Me] Coursera - Practical Reinforcement Learning

Creation Time	Jan. 17, 2024, 2:12 p.m.
Last Access Time	Sept. 14, 2026, 3:06 p.m.
File Size	1.4 GB
Keywords	FreeCoursesOnline Me Coursera - Reinforcement Practical Learning
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Total Files	111
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013.Honor/033. Partial observability.mp4 57.2 MB
019.Planning with Monte Carlo Tree Search/053. Introduction to planning.mp4 51.6 MB
011.Limitations of Tabular Methods/025. Supervised & Reinforcement Learning.mp4 50.6 MB
005.Striving for reward/014. Reward design.mp4 49.7 MB
011.Limitations of Tabular Methods/027. Difficulties with Approximate Methods.mp4 47.0 MB
018.Uncertainty-based exploration/052. Bayesian UCB.mp4 40.8 MB
009.On-policy vs off-policy/023. Accounting for exploration. Expected Value SARSA..mp4 37.7 MB
006.Bellman equations/015. State and Action Value Functions.mp4 37.3 MB
003.Black box optimization/006. Crossentropy method.mp4 36.0 MB
014.Policy-based RL vs Value-based RL/034. Intuition.mp4 34.9 MB
013.Honor/032. More DQN tricks.mp4 33.9 MB
011.Limitations of Tabular Methods/026. Loss functions in value based RL.mp4 33.8 MB
001.Welcome/001. Why should you care.mp4 32.4 MB
007.Generalized Policy Iteration/017. Policy evaluation & improvement.mp4 31.9 MB
014.Policy-based RL vs Value-based RL/036. Policy gradient formalism.mp4 31.6 MB
015.REINFORCE/038. REINFORCE.mp4 31.4 MB
019.Planning with Monte Carlo Tree Search/054. Monte Carlo Tree Search.mp4 30.9 MB
008.Model-free learning/020. Monte-Carlo & Temporal Difference; Q-learning.mp4 30.1 MB
012.Case Study Deep Q-Network/029. DQN the internals.mp4 29.6 MB
008.Model-free learning/019. Model-based vs model-free.mp4 28.8 MB
008.Model-free learning/021. Exploration vs Exploitation.mp4 28.2 MB
004.All the cool stuff that isn't in the base track/011. Evolution strategies log-derivative trick.mp4 27.8 MB
012.Case Study Deep Q-Network/028. DQN bird's eye view.mp4 27.8 MB
010.Experience Replay/024. On-policy vs off-policy; Experience replay.mp4 26.7 MB
016.Actor-critic/042. Case study A3C.mp4 26.1 MB
017.Measuting exploration/045. Recap bandits.mp4 24.7 MB
016.Actor-critic/039. Advantage actor-critic.mp4 24.6 MB
007.Generalized Policy Iteration/018. Policy and value iteration.mp4 24.2 MB
016.Actor-critic/044. Combining supervised & reinforcement learning.mp4 24.0 MB
002.Reinforcement Learning/004. Decision process & applications.mp4 23.0 MB
003.Black box optimization/008. More on approximate crossentropy method.mp4 22.9 MB
018.Uncertainty-based exploration/048. Intuitive explanation.mp4 22.3 MB
018.Uncertainty-based exploration/051. UCB-1.mp4 22.2 MB
017.Measuting exploration/046. Regret measuring the quality of exploration.mp4 21.3 MB
004.All the cool stuff that isn't in the base track/012. Evolution strategies duct tape.mp4 21.2 MB
004.All the cool stuff that isn't in the base track/009. Evolution strategies core idea.mp4 20.9 MB
013.Honor/031. Double Q-learning.mp4 20.5 MB
003.Black box optimization/007. Approximate crossentropy method.mp4 19.3 MB
013.Honor/030. DQN statistical issues.mp4 19.2 MB
017.Measuting exploration/047. The message just repeats. 'Regret, Regret, Regret.'.mp4 18.4 MB
006.Bellman equations/016. Measuring Policy Optimality.mp4 18.1 MB
003.Black box optimization/005. Markov Decision Process.mp4 18.0 MB
002.Reinforcement Learning/003. Multi-armed bandit.mp4 17.9 MB
004.All the cool stuff that isn't in the base track/010. Evolution strategies math problems.mp4 17.7 MB
016.Actor-critic/040. Duct tape zone.mp4 17.5 MB
018.Uncertainty-based exploration/049. Thompson Sampling.mp4 17.1 MB
016.Actor-critic/041. Policy-based vs Value-based.mp4 16.8 MB
018.Uncertainty-based exploration/050. Optimism in face of uncertainty.mp4 16.5 MB
014.Policy-based RL vs Value-based RL/035. All Kinds of Policies.mp4 16.0 MB
004.All the cool stuff that isn't in the base track/013. Blackbox optimization drawbacks.mp4 15.2 MB
016.Actor-critic/043. A3C case study (2 2).mp4 15.0 MB
014.Policy-based RL vs Value-based RL/037. The log-derivative trick.mp4 13.3 MB
001.Welcome/002. Reinforcement learning vs all.mp4 10.8 MB
008.Model-free learning/022. Footnote Monte-Carlo vs Temporal Difference.mp4 10.3 MB
013.Honor/033. Partial observability.srt 27.7 KB
019.Planning with Monte Carlo Tree Search/053. Introduction to planning.srt 25.4 KB
011.Limitations of Tabular Methods/025. Supervised & Reinforcement Learning.srt 25.4 KB
005.Striving for reward/014. Reward design.srt 23.2 KB
011.Limitations of Tabular Methods/027. Difficulties with Approximate Methods.srt 21.9 KB
018.Uncertainty-based exploration/052. Bayesian UCB.srt 19.3 KB
006.Bellman equations/015. State and Action Value Functions.srt 18.2 KB
009.On-policy vs off-policy/023. Accounting for exploration. Expected Value SARSA..srt 17.3 KB
013.Honor/032. More DQN tricks.srt 16.4 KB
014.Policy-based RL vs Value-based RL/034. Intuition.srt 15.6 KB
003.Black box optimization/006. Crossentropy method.srt 15.5 KB
001.Welcome/001. Why should you care.srt 15.4 KB
011.Limitations of Tabular Methods/026. Loss functions in value based RL.srt 15.2 KB
019.Planning with Monte Carlo Tree Search/054. Monte Carlo Tree Search.srt 14.8 KB
008.Model-free learning/020. Monte-Carlo & Temporal Difference; Q-learning.srt 14.5 KB
007.Generalized Policy Iteration/017. Policy evaluation & improvement.srt 14.5 KB
008.Model-free learning/019. Model-based vs model-free.srt 14.1 KB
015.REINFORCE/038. REINFORCE.srt 14.0 KB
008.Model-free learning/021. Exploration vs Exploitation.srt 14.0 KB
014.Policy-based RL vs Value-based RL/036. Policy gradient formalism.srt 13.3 KB
004.All the cool stuff that isn't in the base track/011. Evolution strategies log-derivative trick.srt 12.6 KB
012.Case Study Deep Q-Network/029. DQN the internals.srt 12.3 KB
007.Generalized Policy Iteration/018. Policy and value iteration.srt 12.1 KB
017.Measuting exploration/045. Recap bandits.srt 11.9 KB
016.Actor-critic/044. Combining supervised & reinforcement learning.srt 11.9 KB
016.Actor-critic/039. Advantage actor-critic.srt 11.8 KB
012.Case Study Deep Q-Network/028. DQN bird's eye view.srt 11.4 KB
010.Experience Replay/024. On-policy vs off-policy; Experience replay.srt 11.2 KB
016.Actor-critic/042. Case study A3C.srt 11.1 KB
018.Uncertainty-based exploration/048. Intuitive explanation.srt 10.9 KB
003.Black box optimization/008. More on approximate crossentropy method.srt 10.5 KB
018.Uncertainty-based exploration/051. UCB-1.srt 10.4 KB
017.Measuting exploration/046. Regret measuring the quality of exploration.srt 10.2 KB
002.Reinforcement Learning/004. Decision process & applications.srt 9.7 KB
004.All the cool stuff that isn't in the base track/012. Evolution strategies duct tape.srt 9.7 KB
013.Honor/031. Double Q-learning.srt 9.4 KB
013.Honor/030. DQN statistical issues.srt 9.2 KB
017.Measuting exploration/047. The message just repeats. 'Regret, Regret, Regret.'.srt 8.7 KB
004.All the cool stuff that isn't in the base track/010. Evolution strategies math problems.srt 8.6 KB
006.Bellman equations/016. Measuring Policy Optimality.srt 8.5 KB
003.Black box optimization/005. Markov Decision Process.srt 8.3 KB
003.Black box optimization/007. Approximate crossentropy method.srt 8.2 KB
018.Uncertainty-based exploration/049. Thompson Sampling.srt 7.9 KB
018.Uncertainty-based exploration/050. Optimism in face of uncertainty.srt 7.9 KB
016.Actor-critic/040. Duct tape zone.srt 7.8 KB
014.Policy-based RL vs Value-based RL/035. All Kinds of Policies.srt 7.4 KB
004.All the cool stuff that isn't in the base track/009. Evolution strategies core idea.srt 7.3 KB
004.All the cool stuff that isn't in the base track/013. Blackbox optimization drawbacks.srt 7.3 KB
002.Reinforcement Learning/003. Multi-armed bandit.srt 7.3 KB
016.Actor-critic/041. Policy-based vs Value-based.srt 7.1 KB
016.Actor-critic/043. A3C case study (2 2).srt 6.0 KB
014.Policy-based RL vs Value-based RL/037. The log-derivative trick.srt 5.9 KB
001.Welcome/002. Reinforcement learning vs all.srt 4.9 KB
008.Model-free learning/022. Footnote Monte-Carlo vs Temporal Difference.srt 4.8 KB
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[FreeCoursesOnline.Me].url 133 bytes
[FreeTutorials.Us].url 119 bytes

[FreeCoursesOnline.Me] Coursera - Practical Reinforcement Learning

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