Natural Language Processing (NLP) is applied in reinforcement learning when an agent interacts with environments that involve language, such as text-based games, dialogue systems, or question-answering tasks. In such scenarios, the agent must interpret and generate language, which requires understanding both the semantics and syntax of human language.
In reinforcement learning, NLP is used to process textual or spoken inputs and convert them into states that the agent can use for decision-making. For example, an agent in a text-based environment may receive a description of its surroundings in natural language, and NLP techniques can help it extract actionable information from this description.
NLP also plays a role in language-guided reinforcement learning, where the agent learns to perform tasks or make decisions based on natural language instructions. Using deep learning techniques like transformers or BERT, the agent can learn to map language inputs to appropriate actions or policies, enabling more complex interactions in environments where language is a key component.