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cs.LG2024
A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges
Guiliang Liu, Sheng Xu, Shicheng Liu +3
Inverse Constrained Reinforcement Learning (ICRL) is the task of inferring the implicit constraints that expert agents adhere to, based on their demonstration data. As an emerging…
cs.CV2024
Subject-driven Text-to-Image Generation via Preference-based Reinforcement Learning
Yanting Miao, William Loh, Suraj Kothawade +3
Text-to-image generative models have recently attracted considerable interest, enabling the synthesis of high-quality images from textual prompts. However, these models often lack…
cs.LG2024
Why Online Reinforcement Learning is Causal
Oliver Schulte, Pascal Poupart
Reinforcement learning (RL) and causal modelling naturally complement each other. The goal of causal modelling is to predict the effects of interventions in an environment, while t…