  Out-of-distribution (OOD) detection is a well-studied topic in supervised
learning. Extending the successes in supervised learning methods to the
reinforcement learning (RL) setting, however, is difficult due to the data
generating process - RL agents actively query their environment for data, and
the data are a function of the policy followed by the agent. An agent could
thus neglect a shift in the environment if its policy did not lead it to
explore the aspect of the environment that shifted. Therefore, to achieve safe
and robust generalization in RL, there exists an unmet need for OOD detection
through active experimentation. Here, we attempt to bridge this lacuna by first
defining a causal framework for OOD scenarios or environments encountered by RL
agents in the wild. Then, we propose a novel task: that of Out-of-Task
Distribution (OOTD) detection. We introduce an RL agent that actively
experiments in a test environment and subsequently concludes whether it is OOTD
or not. We name our method GalilAI, in honor of Galileo Galilei, as it
discovers, among other causal processes, that gravitational acceleration is
independent of the mass of a body. Finally, we propose a simple probabilistic
neural network baseline for comparison, which extends extant Model-Based RL. We
find that GalilAI outperforms the baseline significantly. See visualizations of
our method https://galil-ai.github.io/
