Value prediction
16 papers with code • 1 benchmarks • 0 datasets
Latest papers with no code
Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning
Recently, visual representation learning has been shown to be effective and promising for boosting sample efficiency in RL.
Associative Learning Mechanism for Drug-Target Interaction Prediction
The proposed model enhances the corresponding ability to capture the feature information of a single molecular sequence by the drug/protein molecular representation learning module and supplements the information interaction between molecular sequence pairs by the interactive information learning module.
Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network
In this work, we present a method for synthetic CT (sCT) generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction.
AutoDIME: Automatic Design of Interesting Multi-Agent Environments
One approach is to train a second RL agent, called a teacher, who samples environments that are conducive for the learning of student agents.
Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error
In this work, we study the use of the Bellman equation as a surrogate objective for value prediction accuracy.
CoRGi: Content-Rich Graph Neural Networks with Attention
Graph representations of a target domain often project it to a set of entities (nodes) and their relations (edges).
X-model: Improving Data Efficiency in Deep Learning with A Minimax Model
To take the power of both worlds, we propose a novel X-model by simultaneously encouraging the invariance to {data stochasticity} and {model stochasticity}.
Why Should I Trust You, Bellman? Evaluating the Bellman Objective with Off-Policy Data
In this work, we analyze the effectiveness of the Bellman equation as a proxy objective for value prediction accuracy in off-policy evaluation.
Understanding and Leveraging Overparameterization in Recursive Value Estimation
To better understand the utility of deep models in RL we present an analysis of recursive value estimation using overparameterized linear representations that provides useful, transferable findings.
Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis
Self-training based unsupervised domain adaptation (UDA) has shown great potential to address the problem of domain shift, when applying a trained deep learning model in a source domain to unlabeled target domains.
