TP2b.5

Multi-Agent Recurrent Deterministic Policy Gradient with Inter-Agent Communication

Joohyun Cho, Mingxi Liu, Yi Zhou, Rong-Rong Chen, University of Utah, United States
Session:
TP2b: Reinforcement Learning Lecture
Track:
Adaptive Systems, Machine Learning, Data Analytics
Location:
Oak Shelter
Presentation Time:
Tue, 31 Oct, 17:10 - 17:35 PT (UTC -8)
Session Chair:
Talha Bozkus, University of Southern California
Presentation
Discussion
Resources
No resources available.
Session TP2b
TP2b.1: Predictive Estimation for Reinforcement Learning with Time-Varying Reward Functions
Abolfazl Hashemi, Antesh Upadhyay, Purdue University, United States
TP2b.2: Practical Robust Reinforcement Learning Via Adjacent Uncertainty Set
Ukjo Hwang, Songnam Hong, Hanyang University, Republic of Korea
TP2b.3: A Novel Ensemble Q-Learning Algorithm for Policy Optimization in Large-Scale Networks
Talha Bozkus, Urbashi Mitra, University of Southern California, United States
TP2b.4: Reward Attack on Stochastic Bandits with Non-stationary Rewards
Chenye Yang, Guanlin Liu, Lifeng Lai, University of California, Davis, United States
TP2b.5: Multi-Agent Recurrent Deterministic Policy Gradient with Inter-Agent Communication
Joohyun Cho, Mingxi Liu, Yi Zhou, Rong-Rong Chen, University of Utah, United States
Contacts