Statistics for topic multiagent-reinforcement-learning
RepositoryStats tracks 579,584 Github repositories, of these 30 are tagged with the multiagent-reinforcement-learning topic. The most common primary language for repositories using this topic is Python (20).
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OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
A suite of test scenarios for multi-agent 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...
A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
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 suite of test scenarios for multi-agent reinforcement learning.
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
A suite of test scenarios for multi-agent 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...
A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual 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...
A suite of test scenarios for multi-agent reinforcement learning.
A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
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.
Code for our paper: Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning
Code for our paper: Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation
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
A Collection of Multi-Agent Reinforcement Learning (MARL) Resources
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...
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning
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.
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
Multi-Agent Reinforcement Learning (MARL) papers with code
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...
Reading list for adversarial perspective and robustness in deep reinforcement learning.
A solution for Dynamic Spectrum Management in Mission-Critical UAV Networks using Team Q learning as a Multi-Agent Reinforcement Learning Approach