  In the training process of Deep Reinforcement Learning (DRL), agents require
repetitive interactions with the environment. With an increase in training
volume and model complexity, it is still a challenging problem to enhance data
utilization and explainability of DRL training. This paper addresses these
challenges by focusing on the temporal correlations within the time dimension
of time series. We propose a novel approach to segment multivariate time series
into meaningful subsequences and represent the time series based on these
subsequences. Furthermore, the subsequences are employed for causal inference
to identify fundamental causal factors that significantly impact training
outcomes. We design a module to provide feedback on the causality during DRL
training. Several experiments demonstrate the feasibility of our approach in
common environments, confirming its ability to enhance the effectiveness of DRL
training and impart a certain level of explainability to the training process.
Additionally, we extended our approach with priority experience replay
algorithm, and experimental results demonstrate the continued effectiveness of
our approach.
