RL/Multi|Agent RL

Multi-agent reinforcement learning - Wikipedia

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

An introduction to Multi-Agents Reinforcement Learning (MARL)

Deep RL Course · An introduction to Multi-Agents Reinforcement Learning (MARL).

Multi-Agent Reinforcement Learning : r/reinforcementlearning - Reddit

r/reinforcementlearning - Do you agree with this take that Deep RL is going through. 121 upvotes · 56 comments ...

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

Most of the successful RL applications, e.g., the games of Go and Poker, robotics, and autonomous driving, involve the participation of more ...

Multi-agent reinforcement learning: An overview

dynamic tasks online, using algorithms that originate in temporal-difference RL . We discuss the contributions of game theory to MARL , as well as important ...

Paper list of multi-agent reinforcement learning (MARL) - GitHub

Multi-Agent Reinforcement Learning is a very interesting research area, which has strong connections with single-agent RL, multi-agent systems, game theory, ...

Multi-Agent Reinforcement Learning (MARL) and Cooperative AI

Multi-Agent Reinforcement Learning (MARL) is a subfield of reinforcement learning that is becoming increasingly relevant and has been blowing my mind ...

LLM-based Multi-Agent Reinforcement Learning: Current and Future ...

In this letter, we survey the existing LLM-based single-agent and multi-agent RL frameworks and provide potential research directions for future research.

Multi-Agent Training with Different Algorithms - RLlib - Ray

These agents interact in a common game (think agent in the agent-based-simulation setting and not agent=rl agorithm). The client: # Make two ...

Multi-agent reinforcement learning (MARL) versus single ... - YouTube

In this video we compare the performance of both multi-agent reinforcement learning (MARL) and single-agent RL (SARL) in the problem of heat ...

Track: RL: Multi-agent - ICML 2025

Our work provides the first offline RL algorithm for dynamic mechanism design without assuming uniform coverage.

How do I get started with multi-agent reinforcement learning?

After checking the Internet, you will probably find several resources such as. https://github.com/mohammadasghari/dqn-multi-agent-rl ...

Multi-Agent Reinforcement Learning (PPO) with TorchRL Tutorial

Replay buffers are a common building piece of off-policy RL algorithms. In on-policy contexts, a replay buffer is refilled every time a batch of data is ...

How to correctly train policies in multi-agent RL? - AI Stack Exchange

How to correctly train policies in multi-agent RL? · distinct policy: each agent has its own policy: πag_i · shared policy: each agent uses the ...

RL/Multi-Agent RL | Zongqing's Homepage

We investigate multi-agent cooperation from many aspects, including adaptive learning rates, reward sharing, roles, and fairness.

Reinforcement Learning: How to Train an RL Agent from Scratch

This second blog will introduce you to a specific type of RL, called Q-learning, and show you how to code your own RL agent using the example of the game, ...

RL Agent - Reinforcement learning agent - Simulink - MathWorks

Use the RL Agent block to simulate and train a reinforcement learning agent in Simulink. You associate the block with an agent stored in the MATLAB workspace.

eleurent/rl-agents: Implementations of Reinforcement Learning and ...

Usage: experiments evaluate (--train|--test) [--episodes ] [--seed ] [--analyze] experiments benchmark ...

Introduction to Multi-Agent Reinforcement Learning - YouTube

Learn what multi-agent reinforcement learning is and some of the ... MIT 6.S091: Introduction to Deep Reinforcement Learning (Deep RL). Lex ...

Multi-Agent Reinforcement Learning - ScienceDirect.com

MARL is focused on the analysis and control of the behaviors of multiple RL agents that exist in a shared environment.