2020-04-21 11:19:42 -04:00
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---
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id: 5e8f2f13c4cdbe86b5c72da5
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2020-04-24 05:52:42 -05:00
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title: 'Reinforcement Learning With Q-Learning: Example'
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2020-04-21 11:19:42 -04:00
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challengeType: 11
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videoId: RBBSNta234s
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2021-01-13 03:31:00 +01:00
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dashedName: reinforcement-learning-with-q-learning-example
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2020-04-21 11:19:42 -04:00
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---
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2020-11-27 19:02:05 +01:00
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# --question--
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2020-04-21 11:19:42 -04:00
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2020-11-27 19:02:05 +01:00
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## --text--
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2020-04-21 11:19:42 -04:00
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2020-11-27 19:02:05 +01:00
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Fill in the blanks to complete the following Q-Learning equation:
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2020-05-28 22:40:36 +09:00
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2020-11-27 19:02:05 +01:00
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```py
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Q[__A__, __B__] = Q[__A__, __B__] + LEARNING_RATE * (reward + GAMMA * np.max(Q[__C__, :]) - Q[__A__, __B__])
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```
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2020-05-28 22:40:36 +09:00
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2020-11-27 19:02:05 +01:00
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## --answers--
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2020-05-28 22:40:36 +09:00
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2020-11-27 19:02:05 +01:00
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A: `state`
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2020-05-28 22:40:36 +09:00
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2020-11-27 19:02:05 +01:00
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B: `action`
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2020-05-28 22:40:36 +09:00
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2020-11-27 19:02:05 +01:00
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C: `next_state`
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2020-05-28 22:40:36 +09:00
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2020-11-27 19:02:05 +01:00
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---
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2020-05-28 22:40:36 +09:00
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2020-11-27 19:02:05 +01:00
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A: `state`
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2020-05-28 22:40:36 +09:00
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2020-11-27 19:02:05 +01:00
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B: `action`
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C: `prev_state`
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---
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A: `state`
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B: `reaction`
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C: `next_state`
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## --video-solution--
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2020-04-21 11:19:42 -04:00
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2020-11-27 19:02:05 +01:00
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1
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2020-04-21 11:19:42 -04:00
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