  The combination of deep reinforcement learning (DRL) with ensemble methods
has been proved to be highly effective in addressing complex sequential
decision-making problems. This success can be primarily attributed to the
utilization of multiple models, which enhances both the robustness of the
policy and the accuracy of value function estimation. However, there has been
limited analysis of the empirical success of current ensemble RL methods thus
far. Our new analysis reveals that the sample efficiency of previous ensemble
DRL algorithms may be limited by sub-policies that are not as diverse as they
could be. Motivated by these findings, our study introduces a new ensemble RL
algorithm, termed \textbf{T}rajectories-awar\textbf{E} \textbf{E}nsemble
exploratio\textbf{N} (TEEN). The primary goal of TEEN is to maximize the
expected return while promoting more diverse trajectories. Through extensive
experiments, we demonstrate that TEEN not only enhances the sample diversity of
the ensemble policy compared to using sub-policies alone but also improves the
performance over ensemble RL algorithms. On average, TEEN outperforms the
baseline ensemble DRL algorithms by 41\% in performance on the tested
representative environments.
