Reinforcement Learning in NLP

Reinforcement Learning (RL) is a type of machine learning in which an agent learns to make decisions in an environment to achieve a goal through trial-and-error interactions. In the context of Natural Language Processing (NLP), RL can be used to train models to generate natural language text.However, RL for NLP is still an active area of research, and there are many challenges to overcome, such as the high dimensionality of the action space and the difficulty of designing objective functions that capture the desired properties of natural language text.

Reference Links:

• What is Reinforcement Learning? | Definition from TechTarget
• Simple Beginner’s guide to Reinforcement Learning & its implementation
• Take a peek at Deep Reinforcement Learning for NLP
• 10 Real-Life Applications of Reinforcement Learning
• Reinforcement Learning for NLP framework, by Sreeramana Mavilla Software Engineer at Intel