MaDi: Learning to Mask Distractions for Generalization in Visual Deep
  Reinforcement Learning
Bram Grooten Tristan Tomilin Gautham Vasan Matthew E. Taylor A. Rupam Mahmood Meng Fang Mykola Pechenizkiy Decebal Constantin Mocanu
The visual world provides an abundance of information, but many input pixels
received by agents often contain distracting stimuli. Autonomous agents need
the ability to distinguish useful information from task-irrelevant perceptions,
enabling them to generalize to unseen environments with new distractions.
Existing works approach this problem using data augmentation or large auxiliary
networks with additional loss functions. We introduce MaDi, a novel algorithm
that learns to mask distractions by the reward signal only. In MaDi, the
conventional actor-critic structure of deep reinforcement learning agents is
complemented by a small third sibling, the Masker. This lightweight neural
network generates a mask to determine what the actor and critic will receive,
such that they can focus on learning the task. The masks are created
dynamically, depending on the current input. We run experiments on the DeepMind
Control Generalization Benchmark, the Distracting Control Suite, and a real UR5
Robotic Arm. Our algorithm improves the agent's focus with useful masks, while
its efficient Masker network only adds 0.2% more parameters to the original
structure, in contrast to previous work. MaDi consistently achieves
generalization results better than or competitive to state-of-the-art methods.