• Ikvinderpal Singh

Deep Reinforcement Learning (DRL) has shown significant promise in various fields of artificial intelligence, especially in decision-making tasks where agents need to interact with environments. In recent years, DRL has made considerable strides in Natural Language Processing (NLP), where its role is becoming increasingly pivotal. This paper explores the integration of DRL in NLP, focusing on its ability to address challenges such as sequential decision-making, dialogue management, machine translation, text generation, and semantic understanding. We review various DRL-based approaches applied to NLP tasks, discuss their strengths and limitations, and highlight key developments, including reinforcement learning-based reward shaping, policy learning, and exploration strategies. Additionally, we examine how DRL has enabled advancements in conversational AI, question answering systems, and sentiment analysis, among others. Finally, we outline the challenges facing DRL in NLP, such as sample efficiency, interpretability, and computational costs, and propose future research directions for enhancing its effectiveness in NLP applications.

Deep Reinforcement Learning (DRL); Natural Language Processing (NLP); Hierarchical Reinforcement Learning (HRL);

"ROLE OF DEEP REINFORCEMENT LEARNING IN NATURAL LANGUAGE PROCESSING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.n191-n197, April-2023, Available :http://www.jetir.org/papers/JETIR2304F26.pdf

"ROLE OF DEEP REINFORCEMENT LEARNING IN NATURAL LANGUAGE PROCESSING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppn191-n197, April-2023, Available at : http://www.jetir.org/papers/JETIR2304F26.pdf