  Multi-step methods such as Retrace($\lambda$) and $n$-step $Q$-learning have
become a crucial component of modern deep reinforcement learning agents. These
methods are often evaluated as a part of bigger architectures and their
evaluations rarely include enough samples to draw statistically significant
conclusions about their performance. This type of methodology makes it
difficult to understand how particular algorithmic details of multi-step
methods influence learning. In this paper we combine the $n$-step action-value
algorithms Retrace, $Q$-learning, Tree Backup, Sarsa, and $Q(\sigma)$ with an
architecture analogous to DQN. We test the performance of all these algorithms
in the mountain car environment; this choice of environment allows for faster
training times and larger sample sizes. We present statistical analyses on the
effects of the off-policy correction, the backup length parameter $n$, and the
update frequency of the target network on the performance of these algorithms.
Our results show that (1) using off-policy correction can have an adverse
effect on the performance of Sarsa and $Q(\sigma)$; (2) increasing the backup
length $n$ consistently improved performance across all the different
algorithms; and (3) the performance of Sarsa and $Q$-learning was more robust
to the effect of the target network update frequency than the performance of
Tree Backup, $Q(\sigma)$, and Retrace in this particular task.
