Experience replay buffers are a tool used in machine learning, especially in reinforcement learning, to store and reuse past experiences. These experiences are typically the actions an agent took, the state it was in, the reward it received and what happened next. By saving these experiences, the learning process can use them again later, instead…
Reward engineering in reinforcement learning is the process of designing and adjusting the reward signals that guide how an artificial agent learns to make decisions. The reward function tells the agent what behaviours are good or bad by giving positive or negative feedback based on its actions. Careful reward engineering is important because poorly designed…
Multi-Agent Evaluation Scenarios are structured situations or tasks designed to test and measure how multiple autonomous agents interact, solve problems, or achieve goals together. These scenarios help researchers and developers understand the strengths and weaknesses of artificial intelligence systems when they work as a team or compete against each other. By observing agents in controlled…
Reinforcement via user signals refers to improving a system or product by observing how users interact with it. When users click, like, share, or ignore certain items, these actions provide feedback known as user signals. Systems can use these signals to adjust and offer more relevant or useful content, making the experience better for future…
Exploration-Exploitation Strategies are approaches used to balance trying new options with using known, rewarding ones. The aim is to find the best possible outcome by sometimes exploring unfamiliar choices and sometimes sticking with what already works. These strategies are often used in decision-making systems, such as recommendation engines or reinforcement learning, to improve long-term results.
Reward function engineering is the process of designing and adjusting the rules that guide how an artificial intelligence or robot receives feedback for its actions. The reward function tells the AI what is considered good or bad behaviour, shaping its decision-making to achieve specific goals. Careful design is important because a poorly defined reward function…
Deep Deterministic Policy Gradient (DDPG) is a machine learning algorithm used for teaching computers how to make decisions in environments where actions are continuous, such as steering a car or controlling a robot arm. It combines two approaches: learning a policy to choose actions and learning a value function to judge how good those actions…
Imitation learning techniques are methods in artificial intelligence where a computer or robot learns to perform tasks by observing demonstrations, usually from a human expert. Instead of programming every action or rule, the system watches and tries to mimic the behaviour it sees. This approach helps machines learn complex tasks quickly by copying examples, making…
Multi-Objective Reinforcement Learning is a type of machine learning where an agent learns to make decisions by balancing several goals at the same time. Instead of optimising a single reward, the agent considers multiple objectives, which can sometimes conflict with each other. This approach helps create solutions that are better suited to real-life situations where…
Reward sparsity handling refers to techniques used in machine learning, especially reinforcement learning, to address situations where positive feedback or rewards are infrequent or delayed. When an agent rarely receives rewards, it can struggle to learn which actions are effective. By using special strategies, such as shaping rewards or providing hints, learning can be made…