Multi-Agent MuJoCo

'Multi-Agent MuJoCo' is a benchmark that contains various cooperative multi-agent environments designed to evaluate the performance and coordination of algorithms in addressing challenges associated with large joint observation and action spaces in reinforcement learning.

Papers using Multi-Agent MuJoCo (16)

SrSv: Integrating Sequential Rollouts with Sequential Value Estimation for Multi-agent Reinforcement Learning2025 · 2 citesAOAD-MAT: Transformer-based multi-agent deep reinforcement learning model considering agents' order of action decisions2025 · 1 citesDecentralized Diffusion Policy Learning for Enhanced Exploration in Cooperative Multi-agent Reinforcement Learning2026Rethinking Priority Scheduling for Sequential Multi-Agent Decision Making in Stackelberg Games2026Bridging MARL to SARL: An Order-Independent Multi-Agent Transformer via Latent Consensus2026Multi-Agent Model-Based Reinforcement Learning with Joint State-Action Learned Embeddings2026Multi-Agent Deep Reinforcement Learning Under Constrained Communications2026A Historical Interaction-Enhanced Shapley Policy Gradient Algorithm for Multi-Agent Credit Assignment2025HCPO: Hierarchical Conductor-Based Policy Optimization in Multi-Agent Reinforcement Learning2025$K$-Level Policy Gradients for Multi-Agent Reinforcement Learning2025Offline Multi-agent Reinforcement Learning via Sequential Score Decomposition2025Oryx: a Scalable Sequence Model for Many-Agent Coordination in Offline MARL2025Learning Generalizable Skills from Offline Multi-Task Data for Multi-Agent Cooperation2025PMAT: Optimizing Action Generation Order in Multi-Agent Reinforcement Learning2025Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning2024 · 1 citesComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization2024