4 papers
Recommendation Ranking Off-Policy Evaluation under Ranking-Dependent Examination via Examination-Relevance Decomposition
Riki Okamura, Toshiharu Sugawara
Off-policy evaluation, which estimates evaluation policy performance from logged data, is key for recommender ranking policies. However, logged clicks cannot distinguish unexamined…
Deep Neural Network-guided PSO for Tracking a Global Optimal Position in Complex Dynamic Environment
Stephen Raharja, Toshiharu Sugawara
We propose novel particle swarm optimization (PSO) variants incorporated with deep neural networks (DNNs) for particles to pursue globally optimal positions in dynamic environments…
Robust and Efficient Communication in Multi-Agent Reinforcement Learning
Zejiao Liu, Yi Li, Jiali Wang +6
Multi-agent reinforcement learning (MARL) has made significant strides in enabling coordinated behaviors among autonomous agents. However, most existing approaches assume that comm…
Reducing Redundant Computation in Multi-Agent Coordination through Locally Centralized Execution
Yidong Bai, Toshiharu Sugawara
In multi-agent reinforcement learning, decentralized execution is a common approach, yet it suffers from the redundant computation problem. This occurs when multiple agents redunda…