Multi-Agent Particle Environment (MPE)
Emerging12papers using it
2024first seen
The Multi-Agent Particle Environment (MPE) is a benchmark that contains cooperative scenarios used to evaluate multi-agent reinforcement learning (MARL) algorithms and their ability to coordinate among agents.
Papers using Multi-Agent Particle Environment (MPE) (12)
- LERO: LLM-driven Evolutionary framework with Hybrid Rewards and Enhanced
Observation for Multi-Agent Reinforcement LearningPhi-Actor-Critic: Steering General-Sum Games to Pareto-Efficient Correlated EquilibriaMAGIC: Multi-Step Advantage-Gated Causal Influence for Multi-agent Reinforcement LearningDo LLM-derived graph priors improve multi-agent coordination?A Historical Interaction-Enhanced Shapley Policy Gradient Algorithm for Multi-Agent Credit AssignmentHCPO: Hierarchical Conductor-Based Policy Optimization in Multi-Agent Reinforcement LearningDISPATCH -- Decentralized Informed Spatial Planning and Assignment of Tasks for Cooperative Heterogeneous AgentsSAJA: A State-Action Joint Attack Framework on Multi-Agent Deep Reinforcement LearningContinuous-Time Value Iteration for Multi-Agent Reinforcement LearningReaching Consensus in Cooperative Multi-Agent Reinforcement Learning
with Goal ImaginationEnhancing Heterogeneous Multi-Agent Cooperation in Decentralized MARL via GNN-driven Intrinsic RewardsCPIG: Leveraging Consistency Policy with Intention Guidance for
Multi-agent Exploration