Learning robot skills from scratch is often time-consuming, while reusing data promotes sustainability, and improves time efficiency. This study investigates policy transfer across different robotic platforms, focusing on peg-in-hole task using reinforcement learning (RL). Policy training is carried out on two different robots. Their policies are training from scratch, transferred and evaluated for zero-shot and fine-tuning. Results indicate that zero-shot transfer leads to lower success rates and relatively longer task execution times while fine-tuning significantly improves performance with fewer training time-steps. These findings highlight that policy transfer with adaptation techniques improves training efficiency and generalization, reducing the need for extensive retraining and supporting sustainable robotic learning.