Q-Learning
Q-learning is a model-free reinforcement learning algorithm that learns the value of actions in states to find an optimal policy. It uses a Q-table or neural network to estimate expected cumulative rewards for each state-action pair.
Understanding Q-Learning
Q-learning is a foundational reinforcement learning algorithm that learns the optimal action-value function, known as Q-values, which estimates the expected cumulative reward of taking a particular action in a given state and following the optimal policy thereafter. The algorithm works by iteratively updating Q-values using the Bellman equation as the agent interacts with its environment, gradually converging toward optimal decision-making without requiring a model of the environment's dynamics. Deep Q-Networks (DQN), introduced by DeepMind, extended Q-learning by using neural networks to approximate Q-values in high-dimensional state spaces, famously achieving superhuman performance on Atari video games. Q-learning is an off-policy method, meaning it can learn from data generated by different behavioral policies, making it sample-efficient. The algorithm laid groundwork for more advanced reinforcement learning methods and remains widely used in robotics, game AI, and resource optimization problems.
Category
Reinforcement Learning
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Deep Reinforcement Learning
Deep reinforcement learning combines deep neural networks with reinforcement learning algorithms to handle complex, high-dimensional environments. It has achieved superhuman performance in games like Go, chess, and Atari.
Exploration vs Exploitation
Exploration vs exploitation is a fundamental dilemma in reinforcement learning between trying new actions to discover better rewards versus leveraging known good actions. Balancing both is key to optimal long-term performance.
Imitation Learning
Imitation learning is a technique where an AI agent learns to perform tasks by observing and mimicking expert demonstrations. It bridges the gap between supervised learning and reinforcement learning.
Inverse Reinforcement Learning
Inverse reinforcement learning infers the reward function that an expert is optimizing by observing their behavior. It enables AI systems to learn goals and preferences from demonstrations.
Markov Decision Process
A Markov Decision Process (MDP) is a mathematical framework for modeling sequential decision-making problems with probabilistic outcomes. MDPs are the formal foundation for reinforcement learning algorithms.
Minimax
Minimax is a decision-making algorithm used in adversarial settings where one player tries to maximize their score while the other minimizes it. It is the classical approach for game-playing AI systems.
Policy
A policy in reinforcement learning is a function that maps states to actions, defining the agent's behavior strategy. The goal of RL is to learn an optimal policy that maximizes cumulative reward.
Reinforcement Learning
Reinforcement learning is a machine learning paradigm where an agent learns to make decisions by receiving rewards or penalties for its actions in an environment. It has achieved breakthroughs in game playing, robotics, and AI alignment.