Category: Reinforcement Learning Systems

Sim-to-Real Transfer

Sim-to-Real Transfer is a technique in robotics and artificial intelligence where systems are trained in computer simulations and then adapted for use in the real world. The goal is to use the speed, safety, and cost-effectiveness of simulations to develop skills or strategies that can work outside the virtual environment. This process requires addressing differences…

Competitive Multi-Agent Systems

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

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

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…

Curiosity-Driven Exploration

Curiosity-driven exploration is a method where a person or a computer system actively seeks out new things to learn or experience, guided by what seems interesting or unfamiliar. Instead of following strict instructions or rewards, the focus is on exploring unknown areas or ideas out of curiosity. This approach is often used in artificial intelligence…

Intrinsic Motivation in RL

Intrinsic motivation in reinforcement learning refers to a method where an agent is encouraged to explore and learn, not just by external rewards but also by its own curiosity or internal drives. Unlike traditional reinforcement learning, which relies mainly on rewards given for achieving specific goals, intrinsic motivation gives the agent additional signals that reward…

Model-Based Reinforcement Learning

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…

Trust Region Policy Optimisation

Trust Region Policy Optimisation, or TRPO, is a method used in reinforcement learning to help computers learn how to make decisions. It works by ensuring that each learning step does not move too far from the previous strategy, which keeps learning stable and prevents sudden mistakes. By carefully controlling how much the computer’s decision-making policy…

Soft Actor-Critic

Soft Actor-Critic is a type of algorithm used in reinforcement learning that helps computers learn to make decisions by balancing two goals: getting rewards and staying flexible in their choices. It uses a method called maximum entropy, which means it encourages the computer to try different actions rather than always picking the same one. This…

Distributional Reinforcement Learning

Distributional Reinforcement Learning is a method in machine learning where an agent learns not just the average result of its actions, but the full range of possible outcomes and how likely each one is. Instead of focusing solely on expected rewards, this approach models the entire distribution of rewards the agent might receive. This allows…