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When to Trust Your Data: Enhancing Dyna-Style Model-Based Reinforcement Learning With Data Filter
To tackle this issue, we introduce an out-of-distribution (OOD) data filter that removes simulated data from the estimated model that significantly diverges from data collected in the real environment.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
Robust Reward Design for Markov Decision Processes
The problem of reward design examines the interaction between a leader and a follower, where the leader aims to shape the follower's behavior to maximize the leader's payoff by modifying the follower's reward function.
Oxygen vacancies modulated VO2 for neurons and Spiking Neural Network construction
However, its intrinsic insulating state requires the VO2 neuronal device to be driven under large bias voltage, resulting in high power consumption and low frequency.
A Point Cloud-Based Deep Learning Strategy for Protein-Ligand Binding Affinity Prediction
These results suggest that point clouds derived from the PDBbind datasets are useful to evaluate the performance of 3D point clouds-centered deep learning algorithms, which could learn critical protein-ligand interactions from natural evolution or medicinal chemistry and have wide applications in studying protein-ligand interactions.
ResAtom System: Protein and Ligand Affinity Prediction Model Based on Deep Learning
At the same time, we evaluated the performance of a variety of existing scoring functions in combination with ResAtom-Score in the absence of experimentally-determined conformations.
