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• Comment: Source 2 is on arXiv, which is considered unreliable as there is no editorial or peer review needed to publish there. monkey_smashing^keyboards (talk) 21:57, 20 February 2026 (UTC)

Explore Then Commit (ETC) is an algorithm for the Multi-armed bandit problem where you have to do a trade-off between exploration/exploitation.

The Multi-armed bandit problem is a sequential game where one player has to choose at each turn between ${\displaystyle K}$ actions (arms). Behind every arm ${\displaystyle a}$ there is an unknown distribution ${\displaystyle \nu _{a}}$ that lies in a set ${\displaystyle {\mathcal {D}}}$ known by the player (for example, ${\displaystyle {\mathcal {D}}}$ can be the set of Gaussian distributions or Bernoulli distributions).

At each turn ${\displaystyle t}$ the player chooses (pulls) an arm ${\displaystyle a_{t}}$, he then gets an observation ${\displaystyle X_{t}}$ of the distribution ${\displaystyle \nu _{a_{t}}}$.

The goal is to minimize the regret at time ${\displaystyle T}$ that is defined as

${\displaystyle R_{T}:=\sum _{a=1}^{K}\Delta _{a}\mathbb {E} [N_{a}(T)]}$

where

• ${\displaystyle \mu _{a}:=\mathbb {E} [\nu _{a}]}$ is the mean of arm ${\displaystyle a}$
• ${\displaystyle \mu ^{*}:=\max _{a}\mu _{a}}$ is the highest mean
• ${\displaystyle \Delta _{a}:=\mu ^{*}-\mu _{a}}$
• ${\displaystyle N_{a}(t)}$ is the number of pulls of arm ${\displaystyle a}$ up to turn ${\displaystyle t}$
  

The player has to find an algorithm that chooses at each turn ${\displaystyle t}$ which arm to pull based on the previous actions and observations ${\displaystyle (a_{s},X_{s})_{s<t}}$ to minimize the regret ${\displaystyle R_{T}}$.

This is a trade-off problem between exploration to find the best arm (the arm with the highest mean) and exploitation to play as much as possible the arm that we think is the best arm.^[1]

The idea of the algorithm is to explore each arm ${\displaystyle M}$ times. Then for the rest of the game the algorithm exploit by playing the arm with the highest mean. If the horizon ${\displaystyle T}$ is known then the number of exploration ${\displaystyle M}$ can depends on ${\displaystyle T}$.

Adaptations of the algorithm exist^[2] and can be found in the litterature for other settings^[3].

The player choose M
 for each arm i do:
    select arm i M times
    update empirical mean mu[i]
for t from MK+1 to T do:
    select arm a with highestempirical mean mu[a]

When all arms are ${\displaystyle 1}$-sub gaussian, by choosing to explore each arm ${\displaystyle M}$ times, the regret at time ${\displaystyle T}$ verify

${\displaystyle R_{T}\leq M\sum _{i=1}^{K}\Delta _{i}+(T-MK)\sum _{i=1}^{K}\Delta _{i}\exp \left(-{\frac {M\Delta _{i}^{2}}{4}}\right)}$^[1]

We can see the first term as the cost of the exploration

${\displaystyle M\sum _{i=1}^{K}\Delta _{i}}$

And the second term as the cost of not having exploring enough, leading to a probability of not having an optimal arm as the arm with the highest empirical mean.

${\displaystyle (T-MK)\sum _{i=1}^{K}\Delta _{i}\exp \left(-{\frac {M\Delta _{i}^{2}}{4}}\right)}$

Increasing ${\displaystyle M}$ increase the first term while decreasing the second term. The best possible ${\displaystyle M}$ must depends on the ${\displaystyle (\Delta _{i})_{i}}$ which is unknown by the player.

For two arms with Gaussian distribution of variance ${\displaystyle 1}$ it was proved that ETC can't achieve the asymptotic optimal regret of the Equation of Lai-Robbins.^[4]