Elaine Lau

QGFN: Controllable Greediness with Action Values

Stephen Zhewen Lu

Ling Pan

Emmanuel Bengio

Generative Flow Networks (GFlowNets; GFNs) are a family of reward/energy-based generative methods for combinatorial objects, capable of gene… (see more)rating diverse and high-utility samples. However, biasing GFNs towards producing high-utility samples is non-trivial. In this work, we leverage connections between GFNs and reinforcement learning (RL) and propose to combine the GFN policy with an action-value estimate,

2024-06-17

ICML.cc/2024/Workshop/SPIGM (poster)

DGFN: Double Generative Flow Networks

Nikhil Murali Vemgal

Emmanuel Bengio

2023-10-27

NeurIPS.cc/2023/Workshop/GenBio (poster)

An Empirical Study of the Effectiveness of Using a Replay Buffer on Mode Discovery in GFlowNets

Nikhil Murali Vemgal

Reinforcement Learning (RL) algorithms aim to learn an optimal policy by iteratively sampling actions to learn how to maximize the total exp… (see more)ected return,

2023-06-19

ICML.cc/2023/Workshop/SPIGM (poster)

Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning

Flemming Kondrup

Thomas Jiralerspong

Nathan de Lara

Jacob A. Shkrob

My Duc Tran