Action Estimation Enables Decentralized Multiagent Reinforcement Learning

Researchers led by Zhenglong Luo on Jan. 8, 2026 propose an enhanced multiagent reinforcement learning framework that uses action estimation neural networks to infer neighboring agents' behaviors from local observations, avoiding explicit action sharing. The TD3-compatible approach is validated on dual-arm robotic lifting tasks, showing improved robustness and deployment feasibility while reducing communication dependence in information-constrained environments.