  Adopting reasonable strategies is challenging but crucial for an intelligent
agent with limited resources working in hazardous, unstructured, and dynamic
environments to improve the system's utility, decrease the overall cost, and
increase mission success probability. This paper proposes a novel hierarchical
strategy decomposition approach based on the Bayesian chain rule to separate an
intricate policy into several simple sub-policies and organize their
relationships as Bayesian strategy networks (BSN). We integrate this approach
into the state-of-the-art DRL method -- soft actor-critic (SAC) and build the
corresponding Bayesian soft actor-critic (BSAC) model by organizing several
sub-policies as a joint policy. We compare the proposed BSAC method with the
SAC and other state-of-the-art approaches such as TD3, DDPG, and PPO on the
standard continuous control benchmarks -- Hopper-v2, Walker2d-v2, and
Humanoid-v2 -- in MuJoCo with the OpenAI Gym environment. The results
demonstrate that the promising potential of the BSAC method significantly
improves training efficiency.
