Bad Values but Good Behavior: Learning Highly Misspecified Bandits and MDPs
This addresses the challenge of robust decision-making in reinforcement learning for scenarios where model assumptions are imperfect, offering theoretical guarantees for practical algorithms.
The paper tackles the problem of learning optimal policies in bandits and MDPs when reward models are highly misspecified, and shows that under certain structural conditions, algorithms like ε-greedy and LinUCB can provably achieve optimal behavior despite this misspecification.
Parametric, feature-based reward models are employed by a variety of algorithms in decision-making settings such as bandits and Markov decision processes (MDPs). The typical assumption under which the algorithms are analysed is realizability, i.e., that the true values of actions are perfectly explained by some parametric model in the class. We are, however, interested in the situation where the true values are (significantly) misspecified with respect to the model class. For parameterized bandits, contextual bandits and MDPs, we identify structural conditions, depending on the problem instance and model class, under which basic algorithms such as $ε$-greedy, LinUCB and fitted Q-learning provably learn optimal policies under even highly misspecified models. This is in contrast to existing worst-case results for, say misspecified bandits, which show regret bounds that scale linearly with time, and shows that there can be a nontrivially large set of bandit instances that are robust to misspecification.