Search results
Results from the WOW.Com Content Network
Given a training set consisting of examples = (,, ′), where , ′ are observations of a world state from two consecutive time steps , ′ and is an action instance observed in time step , the goal of action model learning in general is to construct an action model , , where is a description of domain dynamics in action description formalism like STRIPS, ADL or PDDL and is a probability ...
The World Institute for Action Learning (WIAL) model was developed by Michael Marquardt, Skipton Leonard, Bea Carson and Arthur Freedman. The model starts with two simple "ground rules" that ensure that statements are related to questions, and grant authority to the coach in order to promote learning. Team members may develop additional ground ...
The theory of Markov decision processes states that if is an optimal policy, we act optimally (take the optimal action) by choosing the action from (,) with the highest action-value at each state, . The action-value function of such an optimal policy ( Q π ∗ {\displaystyle Q^{\pi ^{*}}} ) is called the optimal action-value function and is ...
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text. The largest and most capable LLMs are generative pretrained transformers (GPTs).
Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs. The human user must possess knowledge/expertise in the problem domain, including the ability to consult/research authoritative sources ...
State–action–reward–state–action (SARSA) is an algorithm for learning a Markov decision process policy, used in the reinforcement learning area of machine learning. It was proposed by Rummery and Niranjan in a technical note [ 1 ] with the name "Modified Connectionist Q-Learning" (MCQ-L).
Action selection is a way of characterizing the most basic problem of intelligent systems: what to do next. In artificial intelligence and computational cognitive science, "the action selection problem" is typically associated with intelligent agents and animats—artificial systems that exhibit complex behavior in an agent environment.
The advantage of Greedy GQ is that convergence is guaranteed even when function approximation is used to estimate the action values. Distributional Q-learning is a variant of Q-learning which seeks to model the distribution of returns rather than the expected return of each action. It has been observed to facilitate estimate by deep neural ...