  Multi-step (also called n-step) methods in reinforcement learning (RL) have
been shown to be more efficient than the 1-step method due to faster
propagation of the reward signal, both theoretically and empirically, in tasks
exploiting tabular representation of the value-function. Recently, research in
Deep Reinforcement Learning (DRL) also shows that multi-step methods improve
learning speed and final performance in applications where the value-function
and policy are represented with deep neural networks. However, there is a lack
of understanding about what is actually contributing to the boost of
performance. In this work, we analyze the effect of multi-step methods on
alleviating the overestimation problem in DRL, where multi-step experiences are
sampled from a replay buffer. Specifically building on top of Deep
Deterministic Policy Gradient (DDPG), we propose Multi-step DDPG (MDDPG), where
different step sizes are manually set, and its variant called Mixed Multi-step
DDPG (MMDDPG) where an average over different multi-step backups is used as
update target of Q-value function. Empirically, we show that both MDDPG and
MMDDPG are significantly less affected by the overestimation problem than DDPG
with 1-step backup, which consequently results in better final performance and
learning speed. We also discuss the advantages and disadvantages of different
ways to do multi-step expansion in order to reduce approximation error, and
expose the tradeoff between overestimation and underestimation that underlies
offline multi-step methods. Finally, we compare the computational resource
needs of Twin Delayed Deep Deterministic Policy Gradient (TD3), a state-of-art
algorithm proposed to address overestimation in actor-critic methods, and our
proposed methods, since they show comparable final performance and learning
speed.
