  Current model-based reinforcement learning methods struggle when operating
from complex visual scenes due to their inability to prioritize task-relevant
features. To mitigate this problem, we propose learning Task Informed
Abstractions (TIA) that explicitly separates reward-correlated visual features
from distractors. For learning TIA, we introduce the formalism of Task Informed
MDP (TiMDP) that is realized by training two models that learn visual features
via cooperative reconstruction, but one model is adversarially dissociated from
the reward signal. Empirical evaluation shows that TIA leads to significant
performance gains over state-of-the-art methods on many visual control tasks
where natural and unconstrained visual distractions pose a formidable
challenge.
