Conservative Exploration using Interleaving
This addresses the challenge of safe exploration in combinatorial decision-making for applications like recommendation systems, though it is incremental as it builds on existing bandit frameworks.
The paper tackles the problem of learning the best combinatorial action without ever taking a significantly worse action than a default, by formalizing it as learning in stochastic combinatorial semi-bandits with exchangeable actions and designing efficient algorithms. The result includes bounded n-step regret and real-world experiments showing the algorithms can recommend K most attractive movies without violating production constraints.
In many practical problems, a learning agent may want to learn the best action in hindsight without ever taking a bad action, which is significantly worse than the default production action. In general, this is impossible because the agent has to explore unknown actions, some of which can be bad, to learn better actions. However, when the actions are combinatorial, this may be possible if the unknown action can be evaluated by interleaving it with the production action. We formalize this concept as learning in stochastic combinatorial semi-bandits with exchangeable actions. We design efficient learning algorithms for this problem, bound their n-step regret, and evaluate them on both synthetic and real-world problems. Our real-world experiments show that our algorithms can learn to recommend K most attractive movies without ever violating a strict production constraint, both overall and subject to a diversity constraint.