This deliverable reports all the activities and outcomes related to specifying the requirements for the multi-agent Deep Reinforcement Learning (MA-DRL) scheme of 6G BRAINS. Several applications of DRL are identified by the partners in relation to the general 6G BRAINS use cases defined in D2.1. For each of the DRL applications, it will identify the interfaces between the network and the MA-DRL, as well as the data that need to transit between the MA-DRL scheme and the network. This report lays the ground for the developments of the RL-applications in the MA-DRL scheme
Deep learning has revolutionised artificial intelligence, where the application of increased compute...
Reinforcement Learning (RL) is a framework to deal with decision-making problems with the goal of fi...
Recent development in the field of Artificial Intelligence have dealt with building a winning strate...
Machine Learning (ML) has been a remarkable success in the last few years, which Reinforcement Learn...
Recent successes in applying Deep Learning techniques on Reinforcement Learning algorithms have led ...
This document updates the progress made in the different research areas related to 6G Access Archite...
Recent revolutionary advances in cognitive science using the learning principles of biological brain...
This project was motivated by seeking an AI method towards Artificial General Intelligence (AGI), th...
This paper surveys the field of deep multiagent reinforcement learning (RL). The combination of deep...
Abstract A multi-agent deep reinforcement learning framework is proposed to address link level thro...
Deep Reinforcement Learning (DRL) and Deep Multi-agent Reinforcement Learning (MARL) have achieved s...
This paper surveys the field of deep multiagent reinforcement learning. The combination of deep neur...
A multi-agent methodology is proposed for Decentralized Reinforcement Learning (DRL) of individual b...
<p>A multi-agent methodology is proposed for Decentralized Reinforcement Learning (DRL) of individua...
Reinforcement learning (RL) is a broad family of algorithms for training autonomous agents to collec...
Deep learning has revolutionised artificial intelligence, where the application of increased compute...
Reinforcement Learning (RL) is a framework to deal with decision-making problems with the goal of fi...
Recent development in the field of Artificial Intelligence have dealt with building a winning strate...
Machine Learning (ML) has been a remarkable success in the last few years, which Reinforcement Learn...
Recent successes in applying Deep Learning techniques on Reinforcement Learning algorithms have led ...
This document updates the progress made in the different research areas related to 6G Access Archite...
Recent revolutionary advances in cognitive science using the learning principles of biological brain...
This project was motivated by seeking an AI method towards Artificial General Intelligence (AGI), th...
This paper surveys the field of deep multiagent reinforcement learning (RL). The combination of deep...
Abstract A multi-agent deep reinforcement learning framework is proposed to address link level thro...
Deep Reinforcement Learning (DRL) and Deep Multi-agent Reinforcement Learning (MARL) have achieved s...
This paper surveys the field of deep multiagent reinforcement learning. The combination of deep neur...
A multi-agent methodology is proposed for Decentralized Reinforcement Learning (DRL) of individual b...
<p>A multi-agent methodology is proposed for Decentralized Reinforcement Learning (DRL) of individua...
Reinforcement learning (RL) is a broad family of algorithms for training autonomous agents to collec...
Deep learning has revolutionised artificial intelligence, where the application of increased compute...
Reinforcement Learning (RL) is a framework to deal with decision-making problems with the goal of fi...
Recent development in the field of Artificial Intelligence have dealt with building a winning strate...