The dominant approaches to reinforcement learning rely on a fixed state-action space and reward function that the agent is trying to maximize. During training, the agent is repeatedly reset to a predefined initial state or set of initial states. For example, in the classic RL Mountain Car domain, the agent starts at some point in the valley, continues until it reaches the top of the valley and then resets to somewhere else in the same valley. Learning in this regime is akin to the learning problem faced by Bill Murray in the 1993 movie Groundhog Day in which he repeatedly relives the same day, until he discovers the optimal policy and escapes to the next day. In a more realistic formulation for an RL agent, every day is a new day that may have similarities to the previous day, but the agent never encounters the same state twice. This formulation is a natural fit for robotics problems in which a robot is placed in a room in which it has never previously been, but has seen similar rooms with similar objects in the past. We formalize this problem as optimizing a learning or planning algorithm for a set of environments drawn from a distribution and present two sets of results for learning under these settings. First, we present goal-based action priors for learning how to accelerate planning in environments drawn from the distribution from a training set of environments drawn from the same distribution. Second, we present sample-optimized Rademacher complexity, which is a formal mechanism for assessing the risk in choosing a learning algorithm tuned on a training set drawn from the distribution for use on the entire distribution.