Reinforcement Learning

Action model learning is a method in AI that focuses on predicting the outcomes of actions within a given environment.

Action selection is the process by which an AI determines the best action to take in a given situation.

The Action Value Function evaluates the expected reward for taking a specific action in a given state in reinforcement learning.

Actor-Critic is a reinforcement learning approach combining policy and value function methods.

The interaction between an AI agent and its environment, influencing decision-making and learning.

AlphaStar is an AI developed by DeepMind to play StarCraft II at a professional level, showcasing advanced reinforcement learning techniques.

Batch Reinforcement Learning (Batch RL) is a method where an agent learns from a fixed dataset of experiences.

Boltzmann Exploration is a method for balancing exploration and exploitation in AI, particularly in reinforcement learning.

A combinatorial bandit is a type of algorithm that helps make decisions when multiple options are available simultaneously.

A contextual bandit is a machine learning model that makes decisions based on contextual information to maximize rewards.

A continuous action space allows AI to select from an infinite range of possible actions in decision-making tasks.

The Credit Assignment Problem in AI refers to the challenge of determining which actions are responsible for an outcome.

A Critic Agent evaluates the performance of an AI model by providing feedback on its decisions.

Cumulative reward is the total reward an agent receives over time in reinforcement learning.

Deep Deterministic Policy Gradient is an algorithm used in reinforcement learning for continuous action spaces.

Deep Q-Learning is a reinforcement learning algorithm that combines Q-learning with deep neural networks to optimize decision-making.

Deep Q-Network is a type of AI that learns to make decisions by combining deep learning with Q-learning.

A dense reward provides frequent feedback in reinforcement learning, aiding faster learning and improved performance.

A deterministic policy in AI defines a specific action for each state in a given environment.

A method in reinforcement learning that optimizes policies using gradients for continuous action spaces.

A discrete action space restricts an AI to a finite set of actions.

Distributional Reinforcement Learning focuses on learning the distribution of future rewards rather than just expected values.

Distributional Reinforcement Learning focuses on predicting the full distribution of possible future rewards, rather than just their expected value.

Domain Randomization is a technique used in AI to improve the robustness of models by varying training environments.

A Double Deep Q-Network (DDQN) is an advanced reinforcement learning model that improves stability and performance in decision-making tasks.

Double Q-Learning is an enhancement of Q-Learning that reduces overestimation bias in value function estimates.

A DQN Replay Buffer stores experiences to improve learning efficiency in deep reinforcement learning.

Dueling Q-Networks improve reinforcement learning via parallel action-value estimations.