Body: <p>In a 2-armed-bandit problem, an agent has an opportunity to see n reward for each action. Now the agent should choose actions m times and maximize the expected reward in these m decisions. but it cant see the reward of them. what is the best approach for this?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-11-23T20:51:38.047Z
Comments.Text: Could you clarify - the agent is allowed $n$ time steps where it *can* see the rewards, followed by $m$ time steps over which it wants to maximise the reward, but gets no further feedback?
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-11-24T04:14:58.123Z
Comments.Text: Yes, That's right!
Comments.UserDisplayName: lighting
Tags: <reinforcement-learning><multi-armed-bandits>
Title: Multi-armed bandit problem without getting rewards
UserDisplayName: lighting
ThreadText: # Multi-armed bandit problem without getting rewards
  
  (Asked by: username_0 on 2020-11-23)
  
  In a 2-armed-bandit problem, an agent has an opportunity to see n reward for each action. Now the agent should choose actions m times and maximize the expected reward in these m decisions. but it cant see the reward of them. what is the best approach for this?
  
  **Tags:** reinforcement-learning, multi-armed-bandits
  
  > **Comments:**
  > **username_1**: Could you clarify - the agent is allowed $n$ time steps where it *can* see the rewards, followed by $m$ time steps over which it wants to maximise the reward, but gets no further feedback?  
  > **username_0**: Yes, That's right!  
  
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  ## 0 answers
  
  