Vol. 2, Issue 1, Part A (2025)

Reinforcement Learning (Reinforcement Learning (RL)) has achieved remarkable success in simulationulated domains such as gaming, autonomous control, and optimization; however, its deployment in real-world environments continues to face significant challenges. This study, titled “Reinforcement Learning Beyond Simulation: Real-World Deployment Challenges,” investigates the limitations of simulationulation-trained RL models when transferred to physical systems and evaluates adaptive strategies that bridge the simulationulation-to-reality gap. Using three leading algorithms—Deep Q-Learning (DQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC)—the research employs a two-phase experimental pipeline: simulationulation pre-training followed by real-world adaptation under safety constraints. Statistical analyses, including paired t-tests and ANOVA, revealed substantial performance degradation during direct transfer, with notable improvements achieved after implementing adaptation techniques such as domain randomization, uncertainty modeling, and fine-tuning. The adapted agents demonstrated reduced reward drop, fewer safety violations, and higher success rates across multiple tasks, confirming the importance of structured deployment strategies. The results validate that hybrid RL frameworks integrating simulationulation-based learning with safety-aware real-world updates yield more stable, efficient, and reliable policies. Furthermore, the findings highlight that sample-efficient fine-tuning can achieve significant gains without incurring prohibitive resource costs. The study concludes that bridging the reality gap requires an integrated methodology encompassing robust pre-training, safe adaptation, and continual real-world evaluation. Practical recommendations emphasize adopting controlled adaptation phases, implementing real-time safety monitoring, fostering cross-disciplinary collaboration, and standardizing pre-deployment validation protocols. By addressing both methodological and operational challenges, this research contributes a foundational framework for deploying reinforcement learning agents that are not only intelligent but also safe, interpretable, and sustainable in real-world environments.