Category: Reinforcement Learning Systems

Hierarchical Reinforcement Learning

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…

Actor-Critic Methods

Actor-Critic Methods are a group of algorithms used in reinforcement learning where two components work together to help an agent learn. The actor decides which actions to take, while the critic evaluates how good those actions are based on the current situation. This collaboration allows the agent to improve its decision-making over time by using…

Proximal Policy Optimization (PPO)

Proximal Policy Optimization (PPO) is a type of algorithm used in reinforcement learning to train agents to make good decisions. PPO improves how agents learn by making small, safe updates to their behaviour, which helps prevent them from making drastic changes that could reduce their performance. It is popular because it is relatively easy to…

Monte Carlo Tree Search

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…

Temporal Difference Learning

Temporal Difference Learning is a method used in machine learning where an agent learns how to make decisions by gradually improving its predictions based on feedback from its environment. It combines ideas from dynamic programming and Monte Carlo methods, allowing learning from incomplete sequences of events. This approach helps the agent adjust its understanding over…