  We examine the problem of learning and planning on high-dimensional domains
with long horizons and sparse rewards. Recent approaches have shown great
successes in many Atari 2600 domains. However, domains with long horizons and
sparse rewards, such as Montezuma's Revenge and Venture, remain challenging for
existing methods. Methods using abstraction (Dietterich 2000; Sutton, Precup,
and Singh 1999) have shown to be useful in tackling long-horizon problems. We
combine recent techniques of deep reinforcement learning with existing
model-based approaches using an expert-provided state abstraction. We construct
toy domains that elucidate the problem of long horizons, sparse rewards and
high-dimensional inputs, and show that our algorithm significantly outperforms
previous methods on these domains. Our abstraction-based approach outperforms
Deep Q-Networks (Mnih et al. 2015) on Montezuma's Revenge and Venture, and
exhibits backtracking behavior that is absent from previous methods.
