FILENAME SIZE 001.Welcome/001. Why should you care.mp4 32.4 MB 001.Welcome/001. Why should you care.srt 15.4 KB 001.Welcome/002. Reinforcement learning vs all.mp4 10.8 MB 001.Welcome/002. Reinforcement learning vs all.srt 4.9 KB 002.Reinforcement Learning/003. Multi-armed bandit.mp4 17.9 MB 002.Reinforcement Learning/003. Multi-armed bandit.srt 7.3 KB 002.Reinforcement Learning/004. Decision process & applications.mp4 23 MB 002.Reinforcement Learning/004. Decision process & applications.srt 9.7 KB 003.Black box optimization/005. Markov Decision Process.mp4 18 MB 003.Black box optimization/005. Markov Decision Process.srt 8.3 KB 003.Black box optimization/006. Crossentropy method.mp4 36 MB 003.Black box optimization/006. Crossentropy method.srt 15.5 KB 003.Black box optimization/007. Approximate crossentropy method.mp4 19.3 MB 003.Black box optimization/007. Approximate crossentropy method.srt 8.2 KB 003.Black box optimization/008. More on approximate crossentropy method.mp4 22.9 MB 003.Black box optimization/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 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/010. Evolution strategies math problems.mp4 17.7 MB 004.All the cool stuff that isn't in the base track/010. Evolution strategies math problems.srt 8.6 KB 004.All the cool stuff that isn't in the base track/011. Evolution strategies log-derivative trick.mp4 27.8 MB 004.All the cool stuff that isn't in the base track/011. Evolution strategies log-derivative trick.srt 12.6 KB 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/012. Evolution strategies duct tape.srt 9.7 KB 004.All the cool stuff that isn't in the base track/013. Blackbox optimization drawbacks.mp4 15.2 MB 004.All the cool stuff that isn't in the base track/013. Blackbox optimization drawbacks.srt 7.3 KB 005.Striving for reward/014. Reward design.mp4 49.7 MB 005.Striving for reward/014. Reward design.srt 23.2 KB 006.Bellman equations/015. State and Action Value Functions.mp4 37.3 MB 006.Bellman equations/015. State and Action Value Functions.srt 18.2 KB 006.Bellman equations/016. Measuring Policy Optimality.mp4 18.1 MB 006.Bellman equations/016. Measuring Policy Optimality.srt 8.5 KB 007.Generalized Policy Iteration/017. Policy evaluation & improvement.mp4 31.9 MB 007.Generalized Policy Iteration/017. Policy evaluation & improvement.srt 14.5 KB 007.Generalized Policy Iteration/018. Policy and value iteration.mp4 24.2 MB 007.Generalized Policy Iteration/018. Policy and value iteration.srt 12.1 KB 008.Model-free learning/019. Model-based vs model-free.mp4 28.8 MB 008.Model-free learning/019. Model-based vs model-free.srt 14.1 KB 008.Model-free learning/020. Monte-Carlo & Temporal Difference; Q-learning.mp4 30.1 MB 008.Model-free learning/020. Monte-Carlo & Temporal Difference; Q-learning.srt 14.5 KB 008.Model-free learning/021. Exploration vs Exploitation.mp4 28.2 MB 008.Model-free learning/021. Exploration vs Exploitation.srt 14 KB 008.Model-free learning/022. Footnote Monte-Carlo vs Temporal Difference.mp4 10.3 MB 008.Model-free learning/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 009.On-policy vs off-policy/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 010.Experience Replay/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 011.Limitations of Tabular Methods/025. Supervised & Reinforcement Learning.srt 25.4 KB 011.Limitations of Tabular Methods/026. Loss functions in value based RL.mp4 33.8 MB 011.Limitations of Tabular Methods/026. Loss functions in value based RL.srt 15.2 KB 011.Limitations of Tabular Methods/027. Difficulties with Approximate Methods.mp4 47 MB 011.Limitations of Tabular Methods/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 012.Case Study Deep Q-Network/028. DQN bird's eye view.srt 11.4 KB 012.Case Study Deep Q-Network/029. DQN the internals.mp4 29.6 MB 012.Case Study Deep Q-Network/029. DQN the internals.srt 12.3 KB 013.Honor/030. DQN statistical issues.mp4 19.2 MB 013.Honor/030. DQN statistical issues.srt 9.2 KB 013.Honor/031. Double Q-learning.mp4 20.5 MB 013.Honor/031. Double Q-learning.srt 9.4 KB 013.Honor/032. More DQN tricks.mp4 33.9 MB 013.Honor/032. More DQN tricks.srt 16.4 KB 013.Honor/033. Partial observability.mp4 57.2 MB 013.Honor/033. Partial observability.srt 27.7 KB 014.Policy-based RL vs Value-based RL/034. Intuition.mp4 34.9 MB 014.Policy-based RL vs Value-based RL/034. Intuition.srt 15.6 KB 014.Policy-based RL vs Value-based RL/035. All Kinds of Policies.mp4 16 MB 014.Policy-based RL vs Value-based RL/035. All Kinds of Policies.srt 7.4 KB 014.Policy-based RL vs Value-based RL/036. Policy gradient formalism.mp4 31.6 MB 014.Policy-based RL vs Value-based RL/036. Policy gradient formalism.srt 13.3 KB 014.Policy-based RL vs Value-based RL/037. The log-derivative trick.mp4 13.3 MB 014.Policy-based RL vs Value-based RL/037. The log-derivative trick.srt 5.9 KB 015.REINFORCE/038. REINFORCE.mp4 31.4 MB 015.REINFORCE/038. REINFORCE.srt 14 KB 016.Actor-critic/039. Advantage actor-critic.mp4 24.6 MB 016.Actor-critic/039. Advantage actor-critic.srt 11.8 KB 016.Actor-critic/040. Duct tape zone.mp4 17.5 MB 016.Actor-critic/040. Duct tape zone.srt 7.8 KB 016.Actor-critic/041. Policy-based vs Value-based.mp4 16.8 MB 016.Actor-critic/041. Policy-based vs Value-based.srt 7.1 KB 016.Actor-critic/042. Case study A3C.mp4 26.1 MB 016.Actor-critic/042. Case study A3C.srt 11.1 KB 016.Actor-critic/043. A3C case study (2 2).mp4 15 MB 016.Actor-critic/043. A3C case study (2 2).srt 6 KB 016.Actor-critic/044. Combining supervised & reinforcement learning.mp4 24 MB 016.Actor-critic/044. Combining supervised & reinforcement learning.srt 11.9 KB 017.Measuting exploration/045. Recap bandits.mp4 24.7 MB 017.Measuting exploration/045. Recap bandits.srt 11.9 KB 017.Measuting exploration/046. Regret measuring the quality of exploration.mp4 21.3 MB 017.Measuting exploration/046. Regret measuring the quality of exploration.srt 10.2 KB 017.Measuting exploration/047. The message just repeats. 'Regret, Regret, Regret.'.mp4 18.4 MB 017.Measuting exploration/047. The message just repeats. 'Regret, Regret, Regret.'.srt 8.7 KB 018.Uncertainty-based exploration/048. Intuitive explanation.mp4 22.3 MB 018.Uncertainty-based exploration/048. Intuitive explanation.srt 10.9 KB 018.Uncertainty-based exploration/049. Thompson Sampling.mp4 17.1 MB 018.Uncertainty-based exploration/049. Thompson Sampling.srt 7.9 KB 018.Uncertainty-based exploration/050. Optimism in face of uncertainty.mp4 16.5 MB
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