  The problem of continual learning in the domain of reinforcement learning,
often called non-stationary reinforcement learning, has been identified as an
important challenge to the application of reinforcement learning. We prove a
worst-case complexity result, which we believe captures this challenge:
Modifying the probabilities or the reward of a single state-action pair in a
reinforcement learning problem requires an amount of time almost as large as
the number of states in order to keep the value function up to date, unless the
strong exponential time hypothesis (SETH) is false; SETH is a widely accepted
strengthening of the P $\neq$ NP conjecture. Recall that the number of states
in current applications of reinforcement learning is typically astronomical. In
contrast, we show that just $\textit{adding}$ a new state-action pair is
considerably easier to implement.
