Trending repositories for topic multiagent-reinforcement-learning
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
Multi-Agent Reinforcement Learning (MARL) papers
A suite of test scenarios for multi-agent reinforcement learning.
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning
The TTCP CAGE Challenges are a series of public challenges instigated to foster the development of autonomous cyber defensive agents. This CAGE Challenge 4 (CC4) returns to a defence industry enterpri...
🏆 gym-cooking: Code for "Too many cooks: Bayesian inference for coordinating multi-agent collaboration", Winner of the CogSci 2020 Computational Modeling Prize in High Cognition, and a NeurIPS 2020 C...
Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU (JMLR 2022)
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning
The TTCP CAGE Challenges are a series of public challenges instigated to foster the development of autonomous cyber defensive agents. This CAGE Challenge 4 (CC4) returns to a defence industry enterpri...
Multi-Agent Reinforcement Learning (MARL) papers
🏆 gym-cooking: Code for "Too many cooks: Bayesian inference for coordinating multi-agent collaboration", Winner of the CogSci 2020 Computational Modeling Prize in High Cognition, and a NeurIPS 2020 C...
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
A suite of test scenarios for multi-agent reinforcement learning.
Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU (JMLR 2022)
An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
A suite of test scenarios for multi-agent reinforcement learning.
A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
Multi-Agent Reinforcement Learning (MARL) papers with code
We extend pymarl2 to pymarl3, equipping the MARL algorithms with permutation invariance and permutation equivariance properties. The enhanced algorithm achieves 100% win rates on SMAC-V1 and superior...
Reading list for adversarial perspective and robustness in deep reinforcement learning.
Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU (JMLR 2022)
PantheonRL is a package for training and testing multi-agent reinforcement learning environments. PantheonRL supports cross-play, fine-tuning, ad-hoc coordination, and more.
Multi-Agent Reinforcement Learning (MARL) papers
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning
The TTCP CAGE Challenges are a series of public challenges instigated to foster the development of autonomous cyber defensive agents. This CAGE Challenge 4 (CC4) returns to a defence industry enterpri...
A Collection of Multi-Agent Reinforcement Learning (MARL) Resources
Code for our paper: Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation
Experimentation with Regularized Nash Dynamics on a GPU accelerated game
This is a framework for the research on multi-agent reinforcement learning and the implementation of the experiments in the paper titled by ''Shapley Q-value: A Local Reward Approach to Solve Global R...
🏆 gym-cooking: Code for "Too many cooks: Bayesian inference for coordinating multi-agent collaboration", Winner of the CogSci 2020 Computational Modeling Prize in High Cognition, and a NeurIPS 2020 C...
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning
The TTCP CAGE Challenges are a series of public challenges instigated to foster the development of autonomous cyber defensive agents. This CAGE Challenge 4 (CC4) returns to a defence industry enterpri...
Reading list for adversarial perspective and robustness in deep reinforcement learning.
We extend pymarl2 to pymarl3, equipping the MARL algorithms with permutation invariance and permutation equivariance properties. The enhanced algorithm achieves 100% win rates on SMAC-V1 and superior...
PantheonRL is a package for training and testing multi-agent reinforcement learning environments. PantheonRL supports cross-play, fine-tuning, ad-hoc coordination, and more.
Experimentation with Regularized Nash Dynamics on a GPU accelerated game
Multi-Agent Reinforcement Learning (MARL) papers with code
Code for our paper: Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation
An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
Multi-Agent Reinforcement Learning (MARL) papers
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
A suite of test scenarios for multi-agent reinforcement learning.
A Collection of Multi-Agent Reinforcement Learning (MARL) Resources
A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU (JMLR 2022)
This is a framework for the research on multi-agent reinforcement learning and the implementation of the experiments in the paper titled by ''Shapley Q-value: A Local Reward Approach to Solve Global R...
🏆 gym-cooking: Code for "Too many cooks: Bayesian inference for coordinating multi-agent collaboration", Winner of the CogSci 2020 Computational Modeling Prize in High Cognition, and a NeurIPS 2020 C...
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
A suite of test scenarios for multi-agent reinforcement learning.
A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
Multi-Agent Reinforcement Learning (MARL) papers with code
Code for our paper: Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation
Multi-Agent Reinforcement Learning (MARL) papers
We extend pymarl2 to pymarl3, equipping the MARL algorithms with permutation invariance and permutation equivariance properties. The enhanced algorithm achieves 100% win rates on SMAC-V1 and superior...
A Collection of Multi-Agent Reinforcement Learning (MARL) Resources
Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU (JMLR 2022)
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning
The TTCP CAGE Challenges are a series of public challenges instigated to foster the development of autonomous cyber defensive agents. This CAGE Challenge 4 (CC4) returns to a defence industry enterpri...
Lightweight multi-agent gridworld Gym environment
Reading list for adversarial perspective and robustness in deep reinforcement learning.
🏆 gym-cooking: Code for "Too many cooks: Bayesian inference for coordinating multi-agent collaboration", Winner of the CogSci 2020 Computational Modeling Prize in High Cognition, and a NeurIPS 2020 C...
PantheonRL is a package for training and testing multi-agent reinforcement learning environments. PantheonRL supports cross-play, fine-tuning, ad-hoc coordination, and more.
The Drone Swarm Search project provides an environment for SAR missions built on PettingZoo, where agents, represented by drones, are tasked with locating targets identified as shipwrecked individuals...
The TTCP CAGE Challenges are a series of public challenges instigated to foster the development of autonomous cyber defensive agents. This CAGE Challenge 4 (CC4) returns to a defence industry enterpri...
Code for our paper: Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation
UAV-based Cellular-Communication: Multi-Agent Deep Reinforcement Learning for Interference Management
We extend pymarl2 to pymarl3, equipping the MARL algorithms with permutation invariance and permutation equivariance properties. The enhanced algorithm achieves 100% win rates on SMAC-V1 and superior...
Experimentation with Regularized Nash Dynamics on a GPU accelerated game
Reading list for adversarial perspective and robustness in deep reinforcement learning.
Multi-Agent Reinforcement Learning (MARL) papers with code
Multi-Agent Reinforcement Learning (MARL) papers
A solution for Dynamic Spectrum Management in Mission-Critical UAV Networks using Team Q learning as a Multi-Agent Reinforcement Learning Approach
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
A Collection of Multi-Agent Reinforcement Learning (MARL) Resources
A suite of test scenarios for multi-agent reinforcement learning.
This repo is the implementation of paper ''SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning''.
IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL
PantheonRL is a package for training and testing multi-agent reinforcement learning environments. PantheonRL supports cross-play, fine-tuning, ad-hoc coordination, and more.
An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
Lightweight multi-agent gridworld Gym environment
🏆 gym-cooking: Code for "Too many cooks: Bayesian inference for coordinating multi-agent collaboration", Winner of the CogSci 2020 Computational Modeling Prize in High Cognition, and a NeurIPS 2020 C...