Difference between Epoch-greedy and Epsilon-Greedy algorithm for contextual bandits I am trying to compare Epoch Greedy in Langford & Zhang's paper and the epsilon-greedy approach for contextual bandits as in Chen et al, 2020. My question is that are these the same algorithms?-- one considers minimizes the regret over a hypothesis class while in the other we minimize the expected cumulative regret for the  contextual bandit problem with a finite set of arms and linear regression framework.


Are these the same algorithms?

They are different in how we chunk exploration and exploitation. The primary idea of epoch-greedy is as follows: exploration and exploitation are performed sequentially in chunks, either with apriori fixed $T-$steps or with $\ell-$steps (epochs). It means we do $\ell-$steps exploration before doing $\ell-$steps of exploitation or some variations of this dynamically, where Langford & Zhang analyse the idea in detail. However, in $\epsilon$-greedy, we alternate between exploring and exploiting with the probability of $\epsilon$ and $1-\epsilon$.
