Residual Loss Prediction: Reinforcement Learning with no Incremental Feedback - Daumé III, Hal; Langford, John; Mineiro, Paul; Sharaf, Amr (ICLR 2018 Conference, 2018)
We consider reinforcement learning and bandit structured prediction problems with very sparse loss feedback: only at the end of an episode. We introduce a novel algorithm, RESIDUAL LOSS PREDICTION (RESLOPE), that solves such problems by automatically learning an internal representation of a denser reward function. RESLOPE operates as a reduction to contextual bandits, using its learned loss representation to solve the credit assignment problem, and a contextual bandit oracle to trade-off exploration and exploitation. RESLOPE enjoys a no-regret reduction-style theoretical guarantee and outperforms state of the art reinforcement learning algorithms in both MDP environments and bandit structured prediction settings. TL;DR: We present a novel algorithm for solving reinforcement learning and bandit structured prediction problems with very sparse loss feedback.