<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>

CC BY-SA 4.0
2020-11-23T20:51:38.047Z
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?
Neil Slater
CC BY-SA 4.0
2020-11-24T04:14:58.123Z
Yes, That's right!
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<reinforcement-learning><multi-armed-bandits>
Multi-armed bandit problem without getting rewards
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# 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

