Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that...

Microbial intelligence Multi-agent planning Multi-agent reinforcement learning Pattern-oriented modeling PlatBox Project Reinforcement learning Scientific community...

Q-learning is a reinforcement learning algorithm that trains an agent to assign values to its possible actions based on its current state, without requiring...

Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions...

Deep reinforcement learning (DRL) is a subfield of machine learning that combines principles of reinforcement learning (RL) and deep learning. It involves...

In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves...

onto the server.: v–vi  A mobile agent is a type of software agent, with the feature of autonomy, social ability, learning, and most significantly, mobility...

telecommunications and reinforcement learning. Reinforcement learning utilizes the MDP framework to model the interaction between a learning agent and its environment...

Particularly, reinforcement learning (RL) is essential in assisting agentic AI in making self-directed choices by supporting agents in learning best actions...

Imitation learning is a paradigm in reinforcement learning, where an agent learns to perform a task by supervised learning from expert demonstrations....

DAI is closely related to and a predecessor of the field of multi-agent systems. Multi-agent systems and distributed problem solving are the two main DAI...

(30 October 2019). "Grandmaster level in StarCraft II using multi-agent reinforcement learning". Nature. 575 (7782): 350–354. doi:10.1038/S41586-019-1724-Z...

Junhyuk (2019-11-14). "Grandmaster level in StarCraft II using multi-agent reinforcement learning". Nature. 575 (7782): 350–354. Bibcode:2019Natur.575..350V...

reinforcement learning agents. Intuitively, agents learn to improve their performance by playing "against themselves". In multi-agent reinforcement learning...

Standards and Scaleable Agencies". Infrastructure for Agents, Multi-Agent Systems, and Scalable Multi-Agent Systems. Lecture Notes in Computer Science. Vol...

integrations of reinforcement learning and deep learning architectures have enabled simulation of AI-driven agents in complex multi-agent economic models...

Ten Cent Diet". "A structured prediction approach for generalization in cooperative multi-agent reinforcement learning". GLOP home page GLOP source code...

II using multi-agent reinforcement learning". DeepMind Blog. 31 October 2019. Retrieved 31 October 2019. Gao, Jim (2014). "Machine Learning Applications...

of multi-agent systems or multi-agent simulation in that the goal of ABM is to search for explanatory insight into the collective behavior of agents obeying...

by the end of a finite number of rounds. The multi-armed bandit problem is a classic reinforcement learning problem that exemplifies the exploration–exploitation...

in the field of multi-agent reinforcement learning for a dual purpose: A proof-of-concept to show that modern reinforcement learning algorithms can compete...

single-policy coverage—sufficient for single-agent reinforcement learning—is inadequate for multi-agent settings, requiring unilateral dataset coverage...

Proximal policy optimization (PPO) is a reinforcement learning (RL) algorithm for training an intelligent agent. Specifically, it is a policy gradient...

Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate...

objectives), distributed agents (being executed on physically distinct computers), multi-agent systems (distributed agents that work together to achieve...

Hidden Agenda is used in the field of multi-agent reinforcement learning to show that artificial intelligence agents are able to learn a variety of social...

A multi-agent system is a system created from multiple autonomous elements interacting with each other. These are called agents. In a multi-agent system...

simulation-based optimisation, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In reinforcement learning, the environment is typically...

problem solving and Coordination Multi-agent systems and Software agent and Self-organization Multi-agent reinforcement learning Task Analysis, Environment...

Agents in a Multi-Agent World (MAAMAW-96). Rodriguez, Sebastian; Gaud, Nicolas; Galland, Stéphane (2014). "SARL: A General-Purpose Agent-Oriented Programming...