  We introduce off-policy distributional Q($\lambda$), a new addition to the
family of off-policy distributional evaluation algorithms. Off-policy
distributional Q($\lambda$) does not apply importance sampling for off-policy
learning, which introduces intriguing interactions with signed measures. Such
unique properties distributional Q($\lambda$) from other existing alternatives
such as distributional Retrace. We characterize the algorithmic properties of
distributional Q($\lambda$) and validate theoretical insights with tabular
experiments. We show how distributional Q($\lambda$)-C51, a combination of
Q($\lambda$) with the C51 agent, exhibits promising results on deep RL
benchmarks.
