The paper explores how contingency-awareness improves exploration in reinforcement learning.
problem Improving exploration in reinforcement learning environments with sparse rewards.
method Developed an attentive dynamics model (ADM) to discover controllable elements of observations and used it for state representation in exploration.
result Combining actor-critic algorithms with count-based exploration using the ADM representation achieved impressive results on Atari games.