Autonomous ship collision avoidance requires rule-consistent, interpretable, and effective decisions across different encounter situations. This paper proposes the Logic-Guided Interpretable Deep Reinforcement Learning framework (LG-IDRL) that connects Drools-based COLREGs reasoning with continuous proximal policy optimization (PPO). The rule engine identifies encounter responsibilities and urgency, supplies dynamically activated reward terms, and defines an admissible set for projecting the proposed two-dimensional rudder–acceleration action into the intersection of rule and physical limits when this set is nonempty. In five-seed held-out evaluations, LG-IDRL achieved a task success rate of $92.9\pm 2.1\%$, a collision rate of $1.6\pm 0.4\%$, an automatically and independently assessed behaviour-level rule-compliance rate of $98.7\pm 0.4\%$, and a mean path length of $16.7\pm 0.3$ nm. An adapted recent GRU–PPO–COLREG baseline achieved $89.1\pm 1.7\%$, $3.9\pm 0.9\%$, $95.8\pm 1.2\%$, and $17.4\pm 0.3$ nm, respectively, under the same evaluation protocol. The results characterize performance within six specified two-ship-encounter families; they do not establish guaranteed collision avoidance or generalization to unrestricted maritime traffic.