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Found 8 papers, 2 papers with code
Offline Multi-task Transfer RL with Representational Penalization
We study the problem of representation transfer in offline Reinforcement Learning (RL), where a learner has access to episodic data from a number of source tasks collected a priori, and aims to learn a shared representation to be used in finding a good policy for a target task.
Variance Alignment Score: A Simple But Tough-to-Beat Data Selection Method for Multimodal Contrastive Learning
In recent years, data selection has emerged as a core issue for large-scale visual-language model pretraining, especially on noisy web-curated datasets.
Free from Bellman Completeness: Trajectory Stitching via Model-based Return-conditioned Supervised Learning
Off-policy dynamic programming (DP) techniques such as $Q$-learning have proven to be important in sequential decision-making problems.
Robust Offline Reinforcement Learning -- Certify the Confidence Interval
Currently, reinforcement learning (RL), especially deep RL, has received more and more attention in the research area.
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning
Labeled data are critical to modern machine learning applications, but obtaining labels can be expensive.
A Benchmark for Low-Switching-Cost Reinforcement Learning
A ubiquitous requirement in many practical reinforcement learning (RL) applications, including medical treatment, recommendation system, education and robotics, is that the deployed policy that actually interacts with the environment cannot change frequently.
Deep Q-Learning with Low Switching Cost
We initiate the study on deep reinforcement learning problems that require low switching cost, i. e., small number of policy switches during training.
Hypothesis Transfer Learning via Transformation Functions
We consider the Hypothesis Transfer Learning (HTL) problem where one incorporates a hypothesis trained on the source domain into the learning procedure of the target domain.
