Questions tagged [upper-confidence-bound]
For questions about the upper confidence bound (UCB)-based algorithms or action selection strategies in the context e.g. of bandit or reinforcement learning problems.
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Why aren't exploration techniques, such as UCB or Thompson sampling, used in full RL problems?
Why aren't exploration techniques, such as UCB or Thompson sampling, typically used in bandit problems, used in full RL problems?
Monte Carlo Tree Search may use the above-mentioned methods in its ...
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votes
1answer
59 views
Why do we have two similar action selection strategies for UCB1?
In the literature, there are at least two action selection strategies associated with the UCB1's action selection strategy/policy. For example, in the paper Algorithms for the multi-armed bandit ...
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2answers
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Multi-armed Bandit Notation
I'm doing some introductory research on classical (stochastic) MABs. However, I'm a little confused about the common notation (e.g. in the popular paper of Auer (2002) or Bubeck and Cesa-Bianchi (2012)...
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1answer
116 views
Why am I getting better performance with Thompson sampling than with UCB or $\epsilon$-greedy in a multi-armed bandit problem? [closed]
I ran a test using 3 strategies for multi-armed bandit: UCB, $\epsilon$-greedy, and Thompson sampling.
The results for the rewards I got are as follows:
Thompson sampling had the highest average ...
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2answers
82 views
Should I use exploration strategy in Policy Gradient algorithms?
In policy gradient algorithms the output is a stochastic policy - a probability for each action.
I believe that if I follow the policy (sample an action from the policy) I make use of exploration ...
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1answer
47 views
How do we reach at the formula for UCB action-selection in multi-armed bandit problem?
I came across the formula for Upper Confidence Bound Action Selection (while studying multi-armed bandit problem), which looks like:
$$
A_t \dot{=} \operatorname{argmax}_a \left[ Q_t(a) + c \sqrt{ \...
