How does the Q-learning algorithm work?
Q-learning is a type of reinforcement learning algorithm that was first introduced by Watkins in 1989. It is designed to find the optimal action-selection policy for any given finite Markov decision process (MDP). The goal of Q-learning is to learn the quality of actions, which is represented by the Q-values. These Q-values are used to
- Published in Artificial Intelligence, EITC/AI/ARL Advanced Reinforcement Learning, Introduction, Introduction to reinforcement learning
How are the policy gradients used?
Policy gradient methods are a class of algorithms in reinforcement learning that optimize the policy directly. In reinforcement learning, a policy is a mapping from states of the environment to actions to be taken when in those states. The objective of policy gradient methods is to find the optimal policy that maximizes the expected cumulative
Do deep learning algorithms typically use both supervised and unsupervised learning?
Deep learning, a subset of machine learning, leverages artificial neural networks with multiple layers (hence the term "deep") to model complex patterns in data. These neural networks are designed to automatically learn representations from input data, which can be used for various tasks such as classification, regression, and clustering. Deep learning algorithms can operate under