Innovation and Evaluation of Machine Translation Models Combining Reinforcement Learning Algorithms and RNN

With the rapid acceleration of global integration, machine translation serves as a crucial conduit for cross - cultural interaction. Nevertheless, the existing conventional models demonstrate insufficient resilience when confronted with texts marred by spelling or grammatical inaccuracies, leading to subpar translation outcomes. In response to this, this study puts forward a novel approach that integrates reinforcement learning algorithms with recurrent neural networks (RNNs) to boost the precision, training velocity, and stability of machine translation. This research melds the reinforcement learning mechanism with recurrent neural networks to enhance the translation precision and adaptability of the model. Experimental findings indicate that the model holds substantial advantages in terms of translation accuracy, training speed, and stability. When compared to the traditional model, the model presented in this paper not only remarkably cuts down the translation error rate to approximately 0.056 but also significantly quickens the training process and exhibits greater stability for diverse input texts. The machine translation model introduced herein skillfully combines the merits of reinforcement learning algorithms and RNNs, effectively surmounting the challenges encountered by traditional machine translation and charting new research directions and practical routes in the domain of machine translation. This innovative accomplishment not only offers robust support for enhancing translation quality but also clears a more unobstructed path for cross - cultural communication.