# Multi-armed bandit problem without getting rewards

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?

• 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? Nov 23, 2020 at 20:51
• Yes, That's right! Nov 24, 2020 at 4:14