Revisiting Parameter Sharing in Multi-Agent Deep Reinforcement Learning

By Justin K. Terry, Nathaniel Grammel, Ananth Hari, L. Santos, Benjamin Black. 2020

105 citations recorded by the source corpus.

Subfields: Reinforcement Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Reinforcement Learning

1. Yu Wang
2. Alexandre M. Bayen
3. Chao Yu
4. Eugene Vinitsky
5. Hongyuan Zha
6. Akash Velu
7. Junjie Sheng
8. Bo Jin

Citations in the corpus

• The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games (2021, positive): “agents have identical observation and action spaces), we utilize parameter sharing; past works have shown that this improves the efficiency of learning [5, 32], which is also consistent with our empirical experiences.”
• Dealing with Non-Stationarity in MARL via Trust-Region Decomposition (2021, methodology): “The evaders move randomly, and the pursuers are controlled (Terry et al., 2020a).”