This paper presents an approach for solving the View Planning Problem (VPP) in robotic inspection using Reinforcement Learning (RL). Building on a prior framework, this work takes a significant step forward by integrating a detailed robotic simulation environment with essential modules for trajectory and reachability planning. This allows for the development of an RL agent that not only selects adaptive viewpoints but also considers kinematic constraints and collision-free paths, which are crucial for practical, real-world inspections. The study specifically targets the initial inspection of returned products with high variability, demonstrating the feasibility of RL to manage complex tasks in remanufacturing. The RL-based solution is evaluated using Soft Actor-Critic (SAC) and Proximal Policy Optimization algorithms, with SAC showing superior performance. The learned strategies were validated on a real inspection station, showing the capability of using RL based inspection strategies. This research offers a robust, adaptable solution for inspection challenges, bridging the gap between theoretical models and application-ready inspection systems.