Mixtures of Experts (MoEs) have gained prominence in (self-)supervised
learning due to their enhanced inference efficiency, adaptability to
distributed training, and modularity. Previous research has illustrated that
MoEs can significantly boost Deep Reinforcement Learning (DRL) performance by
expanding the network's parameter count while reducing dormant neurons, thereby
enhancing the model's learning capacity and ability to deal with
non-stationarity. In this work, we shed more light on MoEs' ability to deal
with non-stationarity and investigate MoEs in DRL settings with "amplified"
non-stationarity via multi-task training, providing further evidence that MoEs
improve learning capacity. In contrast to previous work, our multi-task results
allow us to better understand the underlying causes for the beneficial effect
of MoE in DRL training, the impact of the various MoE components, and insights
into how best to incorporate them in actor-critic-based DRL networks. Finally,
we also confirm results from previous work.

以增加参数计数、减少休眠神经元为特点的协同专家模型在深度强化学习中显著提升性能，通过多任务训练模拟非稳态性的扩大，进一步增强学习能力，并探索最佳使用戏剧评论理论网络中的协同专家组件的方法。