Work out multi armed bandit allocation instantly with clear inputs, formula shown and shareable results.
An epsilon-greedy bandit sends a fixed exploration share uniformly across all arms and the remainder to the current leader. Regret is what exploration costs: the conversions an oracle that always played the best arm would have won, minus what the bandit actually earns. Compared with a fixed A/B split, a bandit cuts regret sharply because losing arms stop receiving half the traffic.
Epsilon-greedy allocation
explore traffic = total x epsilon, split evenly across arms; exploit traffic = total - explore, all to the leader; regret = oracle conversions - expected conversions
When the goal is to maximise reward during the experiment rather than to obtain a clean unbiased estimate of each arm's effect. Bandits complicate statistical inference.
The allocation update needs feedback. If conversions arrive days later, the bandit reallocates on incomplete data and can lock onto an arm that only looks good early.