Competitive multi-agent systems are computer-based environments where multiple independent agents interact with each other, often with opposing goals. Each agent tries to achieve its own objectives, which may conflict with the objectives of others. These systems are used to study behaviours such as competition, negotiation, and strategy among agents. They are commonly applied in areas…

Cooperative game theory in AI studies how multiple intelligent agents can work together to achieve shared goals or maximise collective benefits. It focuses on strategies for forming alliances, dividing rewards, and making group decisions fairly and efficiently. This approach helps AI systems collaborate, negotiate, and coordinate actions in environments where working together is more effective…

Multi-Agent Reinforcement Learning (MARL) is a field of artificial intelligence where multiple agents learn to make decisions by interacting with each other and their environment. Each agent aims to maximise its own rewards, which can lead to cooperation, competition, or a mix of both, depending on the context. MARL extends standard reinforcement learning by introducing…

Model-Based Reinforcement Learning is a branch of artificial intelligence where an agent learns not only by trial and error but also by building an internal model of how its environment works. This model helps the agent predict the outcomes of its actions before actually trying them, making learning more efficient. By simulating possible scenarios, the…

Inverse Reinforcement Learning (IRL) is a machine learning technique where an algorithm learns what motivates an expert by observing their behaviour, instead of being told directly what to do. Rather than specifying a reward function upfront, IRL tries to infer the underlying goals or rewards that drive the expert’s actions. This approach is useful for…

Hierarchical Reinforcement Learning (HRL) is an approach in artificial intelligence where complex tasks are broken down into smaller, simpler sub-tasks. Each sub-task can be solved with its own strategy, making it easier to learn and manage large problems. By organising tasks in a hierarchy, systems can reuse solutions to sub-tasks and solve new problems more…

Monte Carlo Tree Search (MCTS) is a computer algorithm used to make decisions, especially in games or situations where there are many possible moves and outcomes. It works by simulating many random possible futures from the current situation, then using the results to decide which move gives the best chance of success. MCTS gradually builds…