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Found 6 papers, 2 papers with code
QGFN: Controllable Greediness with Action Values
Generative Flow Networks (GFlowNets; GFNs) are a family of reward/energy-based generative methods for combinatorial objects, capable of generating diverse and high-utility samples.
DGFN: Double Generative Flow Networks
Deep learning is emerging as an effective tool in drug discovery, with potential applications in both predictive and generative models.
An Empirical Study of the Effectiveness of Using a Replay Buffer on Mode Discovery in GFlowNets
GFlowNets are a special class of algorithms designed to generate diverse candidates, $x$, from a discrete set, by learning a policy that approximates the proportional sampling of $R(x)$.
Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support
Building Agent Assistants that can help improve customer service support requires inputs from industry users and their customers, as well as knowledge about state-of-the-art Natural Language Processing (NLP) technology.
Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning
We design a clinically relevant intermediate reward that encourages continuous improvement of the patient vitals as well as addresses the challenge of sparse reward in RL.
Policy Gradients Incorporating the Future
Reasoning about the future -- understanding how decisions in the present time affect outcomes in the future -- is one of the central challenges for reinforcement learning (RL), especially in highly-stochastic or partially observable environments.
