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Found 8 papers, 7 papers with code
Distilling LLMs' Decomposition Abilities into Compact Language Models
Large Language Models (LLMs) have demonstrated proficiency in their reasoning abilities, yet their large size presents scalability challenges and limits any further customization.
Revisiting the Minimalist Approach to Offline Reinforcement Learning
Recent years have witnessed significant advancements in offline reinforcement learning (RL), resulting in the development of numerous algorithms with varying degrees of complexity.
Anti-Exploration by Random Network Distillation
Despite the success of Random Network Distillation (RND) in various domains, it was shown as not discriminative enough to be used as an uncertainty estimator for penalizing out-of-distribution actions in offline reinforcement learning.
Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows
This Normalizing Flows action encoder is pre-trained in a supervised manner on the offline dataset, and then an additional policy model - controller in the latent space - is trained via reinforcement learning.
Q-Ensemble for Offline RL: Don't Scale the Ensemble, Scale the Batch Size
Training large neural networks is known to be time-consuming, with the learning duration taking days or even weeks.
CORL: Research-oriented Deep Offline Reinforcement Learning Library
CORL is an open-source library that provides thoroughly benchmarked single-file implementations of both deep offline and offline-to-online reinforcement learning algorithms.
Inception Architecture and Residual Connections in Classification of Breast Cancer Histology Images
This paper presents results of applying Inception v4 deep convolutional neural network to ICIAR-2018 Breast Cancer Classification Grand Challenge, part a.
