Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning. This leads to poor performance in theory and often in practice. Some recent approaches (Daumé III et al., 2009; Ross and Bagnell, 2010) provide stronger guarantees in this setting, but remain somewhat unsatisfactory as they train either non-stationary or stochastic policies and require a large number of iterations. In this paper, we propose a new iterative algorithm, which trains a stationary deterministic policy, that can be seen as a no regret algorithm in an online learning setting. We show that any such no regret algorithm, combined with additional reduction assumptions, must find a policy with good performance under the distribution of observations it induces in such sequential settings. We demonstrate that this new approach outperforms previous approaches on two challenging imitation learning problems and a benchmark sequence labeling problem.
You can find more information at paper [1].
- [1] Montague, P. Read. "Reinforcement learning: an introduction, by Sutton, RS and Barto, AG." Trends in cognitive sciences, vol. 3, no. 9, 1999, pp. 360. Elsevier.
- [2] Ross, Stéphane, Gordon, Geoffrey, and Bagnell, Drew. "A reduction of imitation learning and structured prediction to no-regret online learning." In Proceedings of the fourteenth international conference on artificial intelligence and statistics, pages 627–635. JMLR Workshop and Conference Proceedings, 2011.
For further reading on related topics, see the comprehensive book by Sutton & Barto[2].