  This paper formulates hypothesis verification as an RL problem. Specifically,
we aim to build an agent that, given a hypothesis about the dynamics of the
world, can take actions to generate observations which can help predict whether
the hypothesis is true or false. Existing RL algorithms fail to solve this
task, even for simple environments. In order to train the agents, we exploit
the underlying structure of many hypotheses, factorizing them as
{pre-condition, action sequence, post-condition} triplets. By leveraging this
structure we show that RL agents are able to succeed at the task. Furthermore,
subsequent fine-tuning of the policies allows the agent to correctly verify
hypotheses not amenable to the above factorization.
