  Performance evaluations are critical for quantifying algorithmic advances in
reinforcement learning. Recent reproducibility analyses have shown that
reported performance results are often inconsistent and difficult to replicate.
In this work, we argue that the inconsistency of performance stems from the use
of flawed evaluation metrics. Taking a step towards ensuring that reported
results are consistent, we propose a new comprehensive evaluation methodology
for reinforcement learning algorithms that produces reliable measurements of
performance both on a single environment and when aggregated across
environments. We demonstrate this method by evaluating a broad class of
reinforcement learning algorithms on standard benchmark tasks.
