  In many RL applications, once training ends, it is vital to detect any
deterioration in the agent performance as soon as possible. Furthermore, it
often has to be done without modifying the policy and under minimal assumptions
regarding the environment. In this paper, we address this problem by focusing
directly on the rewards and testing for degradation. We consider an episodic
framework, where the rewards within each episode are not independent, nor
identically-distributed, nor Markov. We present this problem as a multivariate
mean-shift detection problem with possibly partial observations. We define the
mean-shift in a way corresponding to deterioration of a temporal signal (such
as the rewards), and derive a test for this problem with optimal statistical
power. Empirically, on deteriorated rewards in control problems (generated
using various environment modifications), the test is demonstrated to be more
powerful than standard tests - often by orders of magnitude. We also suggest a
novel Bootstrap mechanism for False Alarm Rate control (BFAR), applicable to
episodic (non-i.i.d) signal and allowing our test to run sequentially in an
online manner. Our method does not rely on a learned model of the environment,
is entirely external to the agent, and in fact can be applied to detect changes
or drifts in any episodic signal.
