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Found 6 papers, 6 papers with code
BenchMARL: Benchmarking Multi-Agent Reinforcement Learning
The field of Multi-Agent Reinforcement Learning (MARL) is currently facing a reproducibility crisis.
TorchRL: A data-driven decision-making library for PyTorch
PyTorch has ascended as a premier machine learning framework, yet it lacks a native and comprehensive library for decision and control tasks suitable for large development teams dealing with complex real-world data and environments.
System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent Learning
When a system of learning agents is not constrained to homogeneous policies, individual agents may develop diverse behaviors, resulting in emergent complementarity that benefits the system.
POPGym: Benchmarking Partially Observable Reinforcement Learning
Real world applications of Reinforcement Learning (RL) are often partially observable, thus requiring memory.
Heterogeneous Multi-Robot Reinforcement Learning
Through simulations and real-world experiments, we show that: (i) when homogeneous methods fail due to strong heterogeneous requirements, HetGPPO succeeds, and, (ii) when homogeneous methods are able to learn apparently heterogeneous behaviors, HetGPPO achieves higher resilience to both training and deployment noise.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning
VMAS's scenarios prove challenging in orthogonal ways for state-of-the-art MARL algorithms.
