PTDE: Personalized Training With Distilled Execution For Multi-agent Reinforcement Learning

Abstract

Centralized Training with Decentralized Execution (CTDE) has emerged as a widely adopted paradigm in multi-agent reinforcement learning, emphasizing the utilization of global information for learning an enhanced joint \(Q\)-function or centralized critic. In contrast, our investigation delves into harnessing global information to directly enhance individual \(Q\)-functions or individual actors. Notably, we discover that applying identical global information universally across all agents proves insufficient for optimal performance. Consequently, we advocate for the customization of global information tailored to each agent, creating agent-personalized global information to bolster overall performance. Furthermore, we introduce a novel paradigm named Personalized Training with Distilled Execution (PTDE), wherein agent-personalized global information is distilled into the agent's local information. This distilled information is then utilized during decentralized execution, resulting in mi

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keychen2022ptde

Related papers

• GTDE: Grouped Training With Decentralized Execution For Multi-agent Actor-critic (2024)3.58
• CTDS: Centralized Teacher With Decentralized Student For Multi-agent Reinforcement Learning (2022)0.00
• Is Centralized Training With Decentralized Execution Framework Centralized Enough For MARL? (2023)0.00
• A New Framework For Multi-agent Reinforcement Learning -- Centralized Training And Exploration With Decentralized Execution Via Policy Distillation (2019)0.00
• STAS: Spatial-temporal Return Decomposition For Multi-agent Reinforcement Learning (2023)0.00
• Taming Multi-agent Reinforcement Learning With Estimator Variance Reduction (2022)0.00
• Tacit Learning With Adaptive Information Selection For Cooperative Multi-agent Reinforcement Learning (2024)0.00
• Optimistic {\epsilon}-greedy Exploration For Cooperative Multi-agent Reinforcement Learning (2025)0.00