The reinforcement learning framework can be extended to model pragmatic reasoning and other higher-level cognitive processes by incorporating additional layers of decision-making and reasoning into the agents' learning process. Pragmatic reasoning involves considering the context, intentions, and beliefs of the communication partners to infer meaning beyond the literal interpretation of words. By integrating pragmatic reasoning into the agents' learning, they can develop a more nuanced understanding of communication and adapt their strategies based on the context and goals of the interaction.
One way to model pragmatic reasoning in the reinforcement learning framework is to introduce a meta-level of decision-making that takes into account the communicative goals, beliefs, and intentions of the agents. This meta-level reasoning can guide the agents in selecting appropriate communication strategies based on the inferred intentions of their communication partners. By incorporating a theory of mind component, the agents can learn to anticipate and respond to the implicit information conveyed in communication, leading to more effective and contextually appropriate interactions.
Furthermore, higher-level cognitive processes such as theory of mind, perspective-taking, and social reasoning can be integrated into the reinforcement learning framework to enhance the agents' ability to engage in complex and adaptive communication. By simulating these cognitive processes, the agents can learn to interpret and generate communication signals that take into account the mental states and intentions of others, leading to more sophisticated and human-like communication behaviors.