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013.Honor/033. Partial observability.mp4 57.23 MB
019.Planning with Monte Carlo Tree Search/053. Introduction to planning.mp4 51.63 MB
011.Limitations of Tabular Methods/025. Supervised & Reinforcement Learning.mp4 50.61 MB
005.Striving for reward/014. Reward design.mp4 49.7 MB
011.Limitations of Tabular Methods/027. Difficulties with Approximate Methods.mp4 47.03 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 37.73 MB
006.Bellman equations/015. State and Action Value Functions.mp4 37.31 MB
003.Black box optimization/006. Crossentropy method.mp4 36.01 MB
014.Policy-based RL vs Value-based RL/034. Intuition.mp4 34.87 MB
013.Honor/032. More DQN tricks.mp4 33.94 MB
011.Limitations of Tabular Methods/026. Loss functions in value based RL.mp4 33.76 MB
001.Welcome/001. Why should you care.mp4 32.42 MB
007.Generalized Policy Iteration/017. Policy evaluation & improvement.mp4 31.92 MB
014.Policy-based RL vs Value-based RL/036. Policy gradient formalism.mp4 31.56 MB
015.REINFORCE/038. REINFORCE.mp4 31.42 MB
019.Planning with Monte Carlo Tree Search/054. Monte Carlo Tree Search.mp4 30.92 MB
008.Model-free learning/020. Monte-Carlo & Temporal Difference; Q-learning.mp4 30.11 MB
012.Case Study Deep Q-Network/029. DQN the internals.mp4 29.63 MB
008.Model-free learning/019. Model-based vs model-free.mp4 28.78 MB
008.Model-free learning/021. Exploration vs Exploitation.mp4 28.23 MB
004.All the cool stuff that isn't in the base track/011. Evolution strategies log-derivative trick.mp4 27.84 MB
012.Case Study Deep Q-Network/028. DQN bird's eye view.mp4 27.76 MB
010.Experience Replay/024. On-policy vs off-policy; Experience replay.mp4 26.72 MB
016.Actor-critic/042. Case study A3C.mp4 26.09 MB
017.Measuting exploration/045. Recap bandits.mp4 24.66 MB
016.Actor-critic/039. Advantage actor-critic.mp4 24.63 MB
007.Generalized Policy Iteration/018. Policy and value iteration.mp4 24.16 MB
016.Actor-critic/044. Combining supervised & reinforcement learning.mp4 24.02 MB
002.Reinforcement Learning/004. Decision process & applications.mp4 23.01 MB
003.Black box optimization/008. More on approximate crossentropy method.mp4 22.89 MB
018.Uncertainty-based exploration/048. Intuitive explanation.mp4 22.26 MB
018.Uncertainty-based exploration/051. UCB-1.mp4 22.19 MB
017.Measuting exploration/046. Regret measuring the quality of exploration.mp4 21.27 MB
004.All the cool stuff that isn't in the base track/012. Evolution strategies duct tape.mp4 21.17 MB
004.All the cool stuff that isn't in the base track/009. Evolution strategies core idea.mp4 20.86 MB
013.Honor/031. Double Q-learning.mp4 20.46 MB
003.Black box optimization/007. Approximate crossentropy method.mp4 19.27 MB
013.Honor/030. DQN statistical issues.mp4 19.22 MB
017.Measuting exploration/047. The message just repeats. 'Regret, Regret, Regret.'.mp4 18.43 MB
006.Bellman equations/016. Measuring Policy Optimality.mp4 18.08 MB
003.Black box optimization/005. Markov Decision Process.mp4 18 MB
002.Reinforcement Learning/003. Multi-armed bandit.mp4 17.88 MB
004.All the cool stuff that isn't in the base track/010. Evolution strategies math problems.mp4 17.73 MB
016.Actor-critic/040. Duct tape zone.mp4 17.53 MB
018.Uncertainty-based exploration/049. Thompson Sampling.mp4 17.09 MB
016.Actor-critic/041. Policy-based vs Value-based.mp4 16.79 MB
018.Uncertainty-based exploration/050. Optimism in face of uncertainty.mp4 16.54 MB
014.Policy-based RL vs Value-based RL/035. All Kinds of Policies.mp4 16.05 MB
004.All the cool stuff that isn't in the base track/013. Blackbox optimization drawbacks.mp4 15.21 MB
016.Actor-critic/043. A3C case study (2 2).mp4 14.96 MB
014.Policy-based RL vs Value-based RL/037. The log-derivative trick.mp4 13.29 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 27.73 KB
019.Planning with Monte Carlo Tree Search/053. Introduction to planning.srt 25.42 KB
011.Limitations of Tabular Methods/025. Supervised & Reinforcement Learning.srt 25.39 KB
005.Striving for reward/014. Reward design.srt 23.23 KB
011.Limitations of Tabular Methods/027. Difficulties with Approximate Methods.srt 21.86 KB
018.Uncertainty-based exploration/052. Bayesian UCB.srt 19.34 KB
006.Bellman equations/015. State and Action Value Functions.srt 18.24 KB
009.On-policy vs off-policy/023. Accounting for exploration. Expected Value SARSA..srt 17.33 KB
013.Honor/032. More DQN tricks.srt 16.36 KB
014.Policy-based RL vs Value-based RL/034. Intuition.srt 15.56 KB
003.Black box optimization/006. Crossentropy method.srt 15.53 KB
001.Welcome/001. Why should you care.srt 15.41 KB
011.Limitations of Tabular Methods/026. Loss functions in value based RL.srt 15.18 KB
019.Planning with Monte Carlo Tree Search/054. Monte Carlo Tree Search.srt 14.85 KB
008.Model-free learning/020. Monte-Carlo & Temporal Difference; Q-learning.srt 14.54 KB
007.Generalized Policy Iteration/017. Policy evaluation & improvement.srt 14.47 KB
008.Model-free learning/019. Model-based vs model-free.srt 14.07 KB
015.REINFORCE/038. REINFORCE.srt 14 KB
008.Model-free learning/021. Exploration vs Exploitation.srt 13.95 KB
014.Policy-based RL vs Value-based RL/036. Policy gradient formalism.srt 13.28 KB
004.All the cool stuff that isn't in the base track/011. Evolution strategies log-derivative trick.srt 12.64 KB
012.Case Study Deep Q-Network/029. DQN the internals.srt 12.25 KB
007.Generalized Policy Iteration/018. Policy and value iteration.srt 12.05 KB
017.Measuting exploration/045. Recap bandits.srt 11.94 KB
016.Actor-critic/044. Combining supervised & reinforcement learning.srt 11.89 KB
016.Actor-critic/039. Advantage actor-critic.srt 11.81 KB
012.Case Study Deep Q-Network/028. DQN bird's eye view.srt 11.41 KB
010.Experience Replay/024. On-policy vs off-policy; Experience replay.srt 11.2 KB
016.Actor-critic/042. Case study A3C.srt 11.12 KB
018.Uncertainty-based exploration/048. Intuitive explanation.srt 10.92 KB
003.Black box optimization/008. More on approximate crossentropy method.srt 10.45 KB
018.Uncertainty-based exploration/051. UCB-1.srt 10.38 KB
017.Measuting exploration/046. Regret measuring the quality of exploration.srt 10.18 KB
002.Reinforcement Learning/004. Decision process & applications.srt 9.71 KB
004.All the cool stuff that isn't in the base track/012. Evolution strategies duct tape.srt 9.68 KB
013.Honor/031. Double Q-learning.srt 9.44 KB
013.Honor/030. DQN statistical issues.srt 9.18 KB
017.Measuting exploration/047. The message just repeats. 'Regret, Regret, Regret.'.srt 8.72 KB
004.All the cool stuff that isn't in the base track/010. Evolution strategies math problems.srt 8.56 KB
006.Bellman equations/016. Measuring Policy Optimality.srt 8.52 KB
003.Black box optimization/005. Markov Decision Process.srt 8.26 KB
003.Black box optimization/007. Approximate crossentropy method.srt 8.16 KB
018.Uncertainty-based exploration/049. Thompson Sampling.srt 7.89 KB
018.Uncertainty-based exploration/050. Optimism in face of uncertainty.srt 7.87 KB
016.Actor-critic/040. Duct tape zone.srt 7.79 KB
014.Policy-based RL vs Value-based RL/035. All Kinds of Policies.srt 7.41 KB
004.All the cool stuff that isn't in the base track/009. Evolution strategies core idea.srt 7.34 KB
004.All the cool stuff that isn't in the base track/013. Blackbox optimization drawbacks.srt 7.31 KB
002.Reinforcement Learning/003. Multi-armed bandit.srt 7.25 KB
016.Actor-critic/041. Policy-based vs Value-based.srt 7.08 KB
016.Actor-critic/043. A3C case study (2 2).srt 5.95 KB
014.Policy-based RL vs Value-based RL/037. The log-derivative trick.srt 5.91 KB
001.Welcome/002. Reinforcement learning vs all.srt 4.91 KB
008.Model-free learning/022. Footnote Monte-Carlo vs Temporal Difference.srt 4.76 KB
[FTU Forum].url 252 B
[FreeCoursesOnline.Me].url 133 B
[FreeTutorials.Us].url 119 B
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