Efficient Distributed Reinforcement Learning through Agreement

By Paulina Varshavskaya, Leslie Pack Kaelbling, Daniela Rus. 2008

25 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Mechanical Engineering

How researchers see this paper

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

In Artificial Intelligence

1. Davood Hajinezhad
2. Afshin Oroojlooyjadid
3. Jianshu Chen
4. Ali H. Sayed
5. Sergio Valcarcel Macua
6. Santiago Zazo
7. Zhuoran Yang
8. Kaiqing Zhang

In Computer Networks and Communications

1. Ali H. Sayed
2. Davood Hajinezhad
3. Afshin Oroojlooyjadid
4. Jianshu Chen
5. Santiago Zazo
6. Sergio Valcarcel Macua
7. Tamer Basar
8. Zhuoran Yang

In Mechanical Engineering

1. Jianshu Chen
2. Ali H. Sayed
3. Davood Hajinezhad
4. Afshin Oroojlooyjadid

Citations in the corpus

• A review of cooperative multi-agent deep reinforcement learning (2019, methodology): “[143] studies a problem in which each agent has a local observation, executes its own local policy and receives a local reward.”
• Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms (2019, background): “The idea of MARL with networked agents dates back to [189].”
• Distributed Policy Evaluation Under Multiple Behavior Strategies (2013, background): “Other rel ated—but more heuristic—approaches include [28], [29].”