Multi-agent reinforcement learning: A selective overview of theories and algorithms
Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …
has registered tremendous success in solving various sequential decision-making problems …
A review of cooperative multi-agent deep reinforcement learning
A Oroojlooy, D Hajinezhad - Applied Intelligence, 2023 - Springer
Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …
systems in recent years. The aim of this review article is to provide an overview of recent …
Mastering the game of Stratego with model-free multiagent reinforcement learning
We introduce DeepNash, an autonomous agent that plays the imperfect information game
Stratego at a human expert level. Stratego is one of the few iconic board games that artificial …
Stratego at a human expert level. Stratego is one of the few iconic board games that artificial …
Mastering atari, go, chess and shogi by planning with a learned model
Constructing agents with planning capabilities has long been one of the main challenges in
the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge …
the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge …
Pettingzoo: Gym for multi-agent reinforcement learning
This paper introduces the PettingZoo library and the accompanying Agent Environment
Cycle (" AEC") games model. PettingZoo is a library of diverse sets of multi-agent …
Cycle (" AEC") games model. PettingZoo is a library of diverse sets of multi-agent …
Smacv2: An improved benchmark for cooperative multi-agent reinforcement learning
The availability of challenging benchmarks has played a key role in the recent progress of
machine learning. In cooperative multi-agent reinforcement learning, the StarCraft Multi …
machine learning. In cooperative multi-agent reinforcement learning, the StarCraft Multi …
A gentle introduction to reinforcement learning and its application in different fields
Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …
become one of the most important and useful technology. It is a learning method where a …
Douzero: Mastering doudizhu with self-play deep reinforcement learning
Games are abstractions of the real world, where artificial agents learn to compete and
cooperate with other agents. While significant achievements have been made in various …
cooperate with other agents. While significant achievements have been made in various …
Scalable evaluation of multi-agent reinforcement learning with melting pot
JZ Leibo, EA Dueñez-Guzman… - International …, 2021 - proceedings.mlr.press
Existing evaluation suites for multi-agent reinforcement learning (MARL) do not assess
generalization to novel situations as their primary objective (unlike supervised learning …
generalization to novel situations as their primary objective (unlike supervised learning …
Student of Games: A unified learning algorithm for both perfect and imperfect information games
Games have a long history as benchmarks for progress in artificial intelligence. Approaches
using search and learning produced strong performance across many perfect information …
using search and learning produced strong performance across many perfect information …