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001. Why should you care.mp4
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MP4
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32.4 MB
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001. Why should you care.srt
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SRT
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15.4 KB
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002. Reinforcement learning vs all.mp4
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MP4
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10.8 MB
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002. Reinforcement learning vs all.srt
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SRT
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4.9 KB
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003. Multi-armed bandit.mp4
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MP4
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17.9 MB
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003. Multi-armed bandit.srt
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SRT
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7.3 KB
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004. Decision process & applications.mp4
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MP4
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23 MB
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004. Decision process & applications.srt
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SRT
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9.7 KB
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005. Markov Decision Process.mp4
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MP4
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18 MB
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005. Markov Decision Process.srt
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SRT
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8.3 KB
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006. Crossentropy method.mp4
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MP4
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36 MB
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006. Crossentropy method.srt
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SRT
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15.5 KB
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007. Approximate crossentropy method.mp4
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MP4
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19.3 MB
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007. Approximate crossentropy method.srt
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SRT
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8.2 KB
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008. More on approximate crossentropy method.mp4
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MP4
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22.9 MB
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008. More on approximate crossentropy method.srt
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SRT
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10.5 KB
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009. Evolution strategies core idea.mp4
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MP4
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20.9 MB
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009. Evolution strategies core idea.srt
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SRT
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7.3 KB
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010. Evolution strategies math problems.mp4
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MP4
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17.7 MB
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010. Evolution strategies math problems.srt
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SRT
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8.6 KB
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011. Evolution strategies log-derivative trick.mp4
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MP4
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27.8 MB
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011. Evolution strategies log-derivative trick.srt
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SRT
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12.6 KB
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012. Evolution strategies duct tape.mp4
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MP4
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21.2 MB
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012. Evolution strategies duct tape.srt
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SRT
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9.7 KB
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013. Blackbox optimization drawbacks.mp4
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MP4
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15.2 MB
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013. Blackbox optimization drawbacks.srt
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SRT
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7.3 KB
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014. Reward design.mp4
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MP4
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49.7 MB
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014. Reward design.srt
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SRT
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23.2 KB
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015. State and Action Value Functions.mp4
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MP4
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37.3 MB
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015. State and Action Value Functions.srt
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SRT
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18.2 KB
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016. Measuring Policy Optimality.mp4
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MP4
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18.1 MB
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016. Measuring Policy Optimality.srt
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SRT
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8.5 KB
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017. Policy evaluation & improvement.mp4
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MP4
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31.9 MB
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017. Policy evaluation & improvement.srt
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SRT
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14.5 KB
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018. Policy and value iteration.mp4
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MP4
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24.2 MB
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018. Policy and value iteration.srt
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SRT
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12.1 KB
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019. Model-based vs model-free.mp4
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MP4
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28.8 MB
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019. Model-based vs model-free.srt
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SRT
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14.1 KB
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020. Monte-Carlo & Temporal Difference; Q-learning.mp4
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MP4
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30.1 MB
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020. Monte-Carlo & Temporal Difference; Q-learning.srt
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SRT
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14.5 KB
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021. Exploration vs Exploitation.mp4
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MP4
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28.2 MB
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021. Exploration vs Exploitation.srt
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SRT
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14 KB
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022. Footnote Monte-Carlo vs Temporal Difference.mp4
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MP4
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10.3 MB
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022. Footnote Monte-Carlo vs Temporal Difference.srt
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SRT
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4.8 KB
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023. Accounting for exploration. Expected Value SARSA..mp4
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MP4
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37.7 MB
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023. Accounting for exploration. Expected Value SARSA..srt
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SRT
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17.3 KB
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024. On-policy vs off-policy; Experience replay.mp4
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MP4
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26.7 MB
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024. On-policy vs off-policy; Experience replay.srt
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SRT
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11.2 KB
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025. Supervised & Reinforcement Learning.mp4
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MP4
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50.6 MB
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025. Supervised & Reinforcement Learning.srt
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SRT
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25.4 KB
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026. Loss functions in value based RL.mp4
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MP4
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33.8 MB
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026. Loss functions in value based RL.srt
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SRT
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15.2 KB
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027. Difficulties with Approximate Methods.mp4
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MP4
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47 MB
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027. Difficulties with Approximate Methods.srt
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SRT
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21.9 KB
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028. DQN bird's eye view.mp4
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MP4
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27.8 MB
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028. DQN bird's eye view.srt
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SRT
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11.4 KB
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029. DQN the internals.mp4
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MP4
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29.6 MB
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029. DQN the internals.srt
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SRT
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12.3 KB
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030. DQN statistical issues.mp4
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MP4
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19.2 MB
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030. DQN statistical issues.srt
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SRT
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9.2 KB
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031. Double Q-learning.mp4
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MP4
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20.5 MB
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031. Double Q-learning.srt
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SRT
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9.4 KB
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032. More DQN tricks.mp4
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MP4
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33.9 MB
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032. More DQN tricks.srt
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SRT
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16.4 KB
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033. Partial observability.mp4
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MP4
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57.2 MB
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033. Partial observability.srt
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SRT
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27.7 KB
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034. Intuition.mp4
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MP4
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34.9 MB
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034. Intuition.srt
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SRT
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15.6 KB
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035. All Kinds of Policies.mp4
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MP4
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16 MB
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035. All Kinds of Policies.srt
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SRT
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7.4 KB
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036. Policy gradient formalism.mp4
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MP4
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31.6 MB
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036. Policy gradient formalism.srt
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SRT
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13.3 KB
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037. The log-derivative trick.mp4
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MP4
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13.3 MB
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037. The log-derivative trick.srt
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SRT
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5.9 KB
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038. REINFORCE.mp4
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MP4
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31.4 MB
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038. REINFORCE.srt
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SRT
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14 KB
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039. Advantage actor-critic.mp4
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MP4
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24.6 MB
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039. Advantage actor-critic.srt
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SRT
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11.8 KB
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040. Duct tape zone.mp4
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MP4
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17.5 MB
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040. Duct tape zone.srt
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SRT
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7.8 KB
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041. Policy-based vs Value-based.mp4
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MP4
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16.8 MB
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041. Policy-based vs Value-based.srt
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SRT
|
7.1 KB
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042. Case study A3C.mp4
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MP4
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26.1 MB
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042. Case study A3C.srt
|
SRT
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11.1 KB
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043. A3C case study (2 2).mp4
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MP4
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15 MB
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043. A3C case study (2 2).srt
|
SRT
|
6 KB
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044. Combining supervised & reinforcement learning.mp4
|
MP4
|
24 MB
|
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044. Combining supervised & reinforcement learning.srt
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SRT
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11.9 KB
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045. Recap bandits.mp4
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MP4
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24.7 MB
|
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045. Recap bandits.srt
|
SRT
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11.9 KB
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046. Regret measuring the quality of exploration.mp4
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MP4
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21.3 MB
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046. Regret measuring the quality of exploration.srt
|
SRT
|
10.2 KB
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047. The message just repeats. 'Regret, Regret, Regret.'.mp4
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MP4
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18.4 MB
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047. The message just repeats. 'Regret, Regret, Regret.'.srt
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SRT
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8.7 KB
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048. Intuitive explanation.mp4
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MP4
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22.3 MB
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048. Intuitive explanation.srt
|
SRT
|
10.9 KB
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049. Thompson Sampling.mp4
|
MP4
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17.1 MB
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049. Thompson Sampling.srt
|
SRT
|
7.9 KB
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050. Optimism in face of uncertainty.mp4
|
MP4
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16.5 MB
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050. Optimism in face of uncertainty.srt
|
SRT
|
7.9 KB
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051. UCB-1.mp4
|
MP4
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22.2 MB
|
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051. UCB-1.srt
|
SRT
|
10.4 KB
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052. Bayesian UCB.mp4
|
MP4
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40.8 MB
|
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052. Bayesian UCB.srt
|
SRT
|
19.3 KB
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053. Introduction to planning.mp4
|
MP4
|
51.6 MB
|
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053. Introduction to planning.srt
|
SRT
|
25.4 KB
|
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054. Monte Carlo Tree Search.mp4
|
MP4
|
30.9 MB
|
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054. Monte Carlo Tree Search.srt
|
SRT
|
14.8 KB
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[CourseClub.NET].url
|
URL
|
102.4 B
|
|
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[DesireCourse.Com].url
|
URL
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0 B
|
![[Coursera] Practical Reinforcement Learning](/static/img/no-torrent-image.png "[Coursera] Practical Reinforcement Learning")
