Multi|agent Reinforcement Learning

Multi-Agent Reinforcement Learning (PPO) with TorchRL Tutorial

Multi-Agent Reinforcement Learning (PPO) with TorchRL Tutorial · In MAPPO the critic is centralised and takes as input the global state of the system. · In IPPO ...

Multi-Agent Reinforcement Learning

The IIIA is a research center of the CSIC, located on the Autonomous University of Barcelona, specializing in Artificial Intelligence.

How to run multi-agent reinforcement learning in custom ...

To achieve this, you can use 'rlMultiAgentFunctionEnv' function, which was added in the R2023b release. You will have to install the Reinforcement learning ...

Multi-Agent Reinforcement Learning (MARL) algorithms - Medium

This one will cover Multi-Agent environments and algorithms for training such environments in Reinforcement Learning that are based on Q-Learning.

Multi-Agent Reinforcement Learning - MIT Press

Description. The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARL's models, solution concepts, algorithmic ideas, ...

Learning to Communicate with Deep Multi-Agent Reinforcement ...

We are able to demonstrate end-to-end learning of protocols in complex environments inspired by communication riddles and multi-agent computer vision problems.

Efficient and scalable reinforcement learning for large-scale network ...

Here we develop a model-based decentralized policy optimization framework, which can be efficiently deployed in multi-agent systems.

Multi-Agent Reinforcement Learning and Bandit Learning

While the basic (single-agent) reinforcement learning problem has been the subject of intense recent investigation — including development of ...

Efficient Multi-agent Reinforcement Learning by Planning

Multi-agent reinforcement learning (MARL) algorithms have accomplished remarkable breakthroughs in solving large-scale decision-making tasks.

Robust Multi-Agent Reinforcement Learning with Model Uncertainty

This is naturally motivated by some multi-agent applications where each agent may not have perfectly accurate knowledge of the model, e.g., all the reward ...

Multi-agent Reinforcement Learning - Papers With Code

The target of **Multi-agent Reinforcement Learning** is to solve complex problems by integrating multiple agents that focus on different sub-tasks.

Track: RL: Multi-agent - ICML 2025

We propose H-MARL (Hallucinated Multi-Agent Reinforcement Learning), a novel sample-efficient algorithm that can efficiently balance exploration, i.e., learning ...

RACE: Improve Multi-Agent Reinforcement Learning with ...

RACE maintains a MARL team and a population of EA teams. To enable efficient knowledge sharing and policy exploration, RACE decomposes the policies of different ...

Consensus Learning for Cooperative Multi-Agent Reinforcement ...

We propose consensus learning for cooperative multi-agent reinforcement learning in this study. Although based on local observations, different agents can ...

Deep multiagent reinforcement learning: challenges and directions

Reinforcement learning (RL) is a machine-learning method in which one agent or a group of agents maximises its long-term return through repeated ...

Multi-Agent Reinforcement Learning: Independent versus ...

Semantic Scholar extracted view of "Multi-Agent Reinforcement Learning: Independent versus Cooperative Agents" by Ming Tan.

Multi-Agent Reinforcement Learning: A Selective Overview of ...

We review the theoretical results of MARL algorithms mainly within two representative frameworks, Markov/stochastic games and extensive-form games.

MARLlib: A Scalable and Efficient Library For Multi-agent ...

Abstract. A significant challenge facing researchers in the area of multi-agent reinforcement learning. (MARL) pertains to the identification of a library ...

Reinforcement learning - GeeksforGeeks

Unlike supervised learning, which relies on a training dataset with predefined answers, RL involves learning through experience. In RL, an agent ...

Multi-Agent Learning Seminar

The Multi-Agent Learning Seminar is a forum for discussing research related to multi-agent learning, including (deep) multi-agent reinforcement learning (MARL).