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20212023
most citedDFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning

9 citations · 16 across the 5 of their papers we have counts for

collaborators

5 papers

cs.MA2023

A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning

Wei-Fang Sun, Cheng-Kuang Lee, Simon See +1

In fully cooperative multi-agent reinforcement learning (MARL) settings, environments are highly stochastic due to the partial observability of each agent and the continuously chan…

cs.LG2023★ 1 cited

Training Energy-Based Normalizing Flow with Score-Matching Objectives

Chen-Hao Chao, Wei-Fang Sun, Yen-Chang Hsu +2

In this paper, we establish a connection between the parameterization of flow-based and energy-based generative models, and present a new flow-based modeling approach called energy…

cs.LG2022

On Investigating the Conservative Property of Score-Based Generative Models

Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng +1

Existing Score-Based Models (SBMs) can be categorized into constrained SBMs (CSBMs) or unconstrained SBMs (USBMs) according to their parameterization approaches. CSBMs model probab…

cs.LG2022★ 6 cited

Denoising Likelihood Score Matching for Conditional Score-based Data Generation

Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng +6

Many existing conditional score-based data generation methods utilize Bayes' theorem to decompose the gradients of a log posterior density into a mixture of scores. These methods f…

cs.MA2021★ 9 cited

DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning

Wei-Fang Sun, Cheng-Kuang Lee, Chun-Yi Lee

In fully cooperative multi-agent reinforcement learning (MARL) settings, the environments are highly stochastic due to the partial observability of each agent and the continuously…