  Discrete-action reinforcement learning algorithms often falter in tasks with
high-dimensional discrete action spaces due to the vast number of possible
actions. A recent advancement leverages value-decomposition, a concept from
multi-agent reinforcement learning, to tackle this challenge. This study delves
deep into the effects of this value-decomposition, revealing that whilst it
curtails the over-estimation bias inherent to Q-learning algorithms, it
amplifies target variance. To counteract this, we present an ensemble of
critics to mitigate target variance. Moreover, we introduce a regularisation
loss that helps to mitigate the effects that exploratory actions in one
dimension can have on the value of optimal actions in other dimensions. Our
novel algorithm, REValueD, tested on discretised versions of the DeepMind
Control Suite tasks, showcases superior performance, especially in the
challenging humanoid and dog tasks. We further dissect the factors influencing
REValueD's performance, evaluating the significance of the regularisation loss
and the scalability of REValueD with increasing sub-actions per dimension.
