Reinforcement learning (RL) agents optimize only the specified features and are indifferent to anything left out inadvertently. This means that we must not only tell a household robot what to do, but also the much larger space of what not to do. It is easy to forget these preferences, since we are so used to having them satisfied. Our key insight is that when a robot is deployed in an environment that humans act in, the state of the environment is already optimized for what humans want. We can therefore use this implicit information from the state to fill in the blanks. We develop an algorithm based on Maximum Causal Entropy IRL and use it to evaluate the idea in a suite of proof-of-concept environments designed to show its properties. We find that information from the initial state can be used to infer both side effects that should be avoided as well as preferences for how the environment should be organized.