Category: Deep Learning

Prioritised Experience Replay

Prioritised Experience Replay is a technique used in machine learning, particularly in reinforcement learning, to improve how an algorithm learns from past experiences. Instead of treating all previous experiences as equally important, this method ranks them based on how much they can help the learning process. The algorithm then focuses more on experiences that are…

Double Deep Q-Learning

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 (DQN)

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…

On-Policy Reinforcement Learning

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…

Conditional Generative Models

Conditional generative models are a type of artificial intelligence that creates new data based on specific input conditions or labels. Instead of generating random outputs, these models use extra information to guide what they produce. This allows for more control over the type of data generated, such as producing images of a certain category or…

Equivariant Neural Networks

Equivariant neural networks are a type of artificial neural network designed so that their outputs change predictably when the inputs are transformed. For example, if you rotate or flip an image, the network’s response changes in a consistent way that matches the transformation. This approach helps the network recognise patterns or features regardless of their…