Description: The ‘Bargaining Problem’ in the context of reinforcement learning refers to situations where multiple agents must interact and reach an agreement on a set of decisions or actions. This type of problem is fundamental in scenarios where the interests of the agents may be conflicting or complementary, requiring a negotiation process to maximize individual or collective rewards. In this framework, each agent must learn not only to optimize its own strategy but also to anticipate and respond to the actions of others. The main characteristics of this problem include the need for communication among agents, adaptation to opponents’ strategies, and the search for a balance that allows for a beneficial outcome for all involved. The relevance of the bargaining problem lies in its applicability in various fields, such as economics, robotics, multi-agent systems, and artificial intelligence, where collaboration and competition are essential for task success. As agents learn to negotiate, they develop skills that enable them to improve their performance in dynamic and complex environments, making this problem an active area of research and great interest in the field of reinforcement learning.
