Dueling DQN is a type of deep reinforcement learning algorithm that improves upon traditional Deep Q-Networks by separating the estimation of the value of a state from the advantages of possible actions. This means it learns not just how good an action is in a particular state, but also how valuable the state itself is,…
Double Deep Q-Learning is an improvement on the Deep Q-Learning algorithm used in reinforcement learning. It helps computers learn to make better decisions by reducing errors that can happen when estimating future rewards. By using two separate networks to choose and evaluate actions, it avoids overestimating how good certain options are, making learning more stable…
Deep Q-Networks, or DQNs, are a type of artificial intelligence that helps computers learn how to make decisions by using deep learning and reinforcement learning together. DQNs use neural networks to estimate the value of taking certain actions in different situations, which helps the computer figure out what to do next. This method allows machines…
Q-Learning variants are different versions or improvements of the basic Q-Learning algorithm, which is a method used in reinforcement learning to help computers learn the best actions to take in a given situation. These variants are designed to address limitations of the original algorithm, such as slow learning speed or instability. By making changes to…
Policy Gradient Methods are a type of approach in reinforcement learning where an agent learns to make decisions by directly improving its decision-making policy. Instead of trying to estimate the value of each action, these methods adjust the policy itself to maximise rewards over time. The agent uses feedback from its environment to gradually tweak…
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…
Reward shaping is a technique used in reinforcement learning where additional signals are given to an agent to guide its learning process. By providing extra rewards or feedback, the agent can learn desired behaviours more quickly and efficiently. This helps the agent avoid unproductive actions and focus on strategies that lead to the main goal.
On-policy reinforcement learning is a method where an agent learns to make decisions by following and improving the same policy that it uses to interact with its environment. The agent updates its strategy based on the actions it actually takes, rather than exploring alternative possibilities. This approach helps the agent gradually improve its behaviour through…
Off-policy reinforcement learning is a method where an agent learns the best way to make decisions by observing actions that may not be the ones it would choose itself. This means the agent can learn from data collected by other agents or from past actions, rather than only from its own current behaviour. This approach…
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…