Category: Reinforcement Learning Systems

RL for Real-World Robotics

Reinforcement Learning (RL) for Real-World Robotics is a branch of artificial intelligence that teaches robots to learn from their own experiences through trial and error. Instead of following pre-programmed instructions, robots use RL to figure out the best way to complete tasks by receiving feedback based on their actions. This approach allows robots to adapt…

RL with Human Feedback

Reinforcement Learning with Human Feedback (RLHF) is a method where artificial intelligence systems learn by receiving guidance from people instead of relying only on automatic rewards. This approach helps AI models understand what humans consider to be good or useful behaviour. By using feedback from real users or experts, the AI can improve its responses…

Exploration-Exploitation Trade-Offs

Exploration-exploitation trade-offs are decisions about whether to try new things or stick with what is already known to work well. In many situations, like learning or making choices, there is a balance between exploring new options to gain more information and exploiting what has already been proven to give good results. Finding the right balance…

Reward Signal Shaping

Reward signal shaping is a technique used in machine learning, especially in reinforcement learning, to guide an agent towards better behaviour by adjusting the feedback it receives. Instead of only giving a reward when the final goal is reached, extra signals are added along the way to encourage progress. This helps the agent learn faster…

RL for Continuous Action Spaces

Reinforcement Learning (RL) for Continuous Action Spaces is a branch of machine learning where an agent learns to make decisions in environments where actions can take any value within a range, instead of being limited to a set of discrete choices. This approach is important for problems where actions are naturally measured in real numbers,…

Distributed RL Algorithms

Distributed reinforcement learning (RL) algorithms are methods where multiple computers or processors work together to train an RL agent more efficiently. Instead of a single machine running all the computations, tasks like collecting data, updating the model, and evaluating performance are divided among several machines. This approach can handle larger problems, speed up training, and…

Hierarchical Policy Learning

Hierarchical policy learning is a method in machine learning where complex tasks are broken down into simpler sub-tasks. Each sub-task is handled by its own policy, and a higher-level policy decides which sub-policy to use at each moment. This approach helps systems learn and perform complicated behaviours more efficiently by organising actions in layers, making…

Multi-Objective Reinforcement Learning

Multi-Objective Reinforcement Learning is a type of machine learning where an agent learns to make decisions that balance several goals at the same time, rather than just one. Instead of maximising a single reward, the agent considers multiple objectives, which might sometimes compete with each other. This approach is useful in situations where trade-offs must…

Adaptive Exploration Strategies

Adaptive exploration strategies are methods used by algorithms or systems to decide how to search or try new options based on what has already been learned. Instead of following a fixed pattern, these strategies adjust their behaviour depending on previous results, aiming to find better solutions more efficiently. This approach helps in situations where blindly…