MAPL trains quadruped locomotion policies from LLM-generated multi-objective trajectory preferences and matches or exceeds expert-designed reward performance in four environments without manual reward engineering.
PEBBLE: Feedback-efficient interactive reinforcement learning via relabeling experience and unsupervised pre-training
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
SENIOR improves feedback efficiency and policy learning speed in PbRL by combining motion-distinction query selection via kernel density estimation with preference-guided intrinsic rewards, showing gains on simulated and real robot tasks.
The Hidden Utility Bandit (HUB) framework models teacher heterogeneity in reward learning and supports active teacher selection algorithms that outperform baselines in paper recommendation and COVID-19 vaccine testing domains.
Themis is an XAI-enabled framework for RL from human feedback that supports 200+ environments and includes a scalable cloud platform for collecting human preferences.
citing papers explorer
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MAPL: Multi-Objective Preference Learning for Robot Locomotion
MAPL trains quadruped locomotion policies from LLM-generated multi-objective trajectory preferences and matches or exceeds expert-designed reward performance in four environments without manual reward engineering.
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SENIOR: Efficient Query Selection and Preference-Guided Exploration in Preference-based Reinforcement Learning
SENIOR improves feedback efficiency and policy learning speed in PbRL by combining motion-distinction query selection via kernel density estimation with preference-guided intrinsic rewards, showing gains on simulated and real robot tasks.
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Active teacher selection for reward learning
The Hidden Utility Bandit (HUB) framework models teacher heterogeneity in reward learning and supports active teacher selection algorithms that outperform baselines in paper recommendation and COVID-19 vaccine testing domains.
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Themis: An explainable AI-enabled framework for Reinforcement Learning with Human Feedback
Themis is an XAI-enabled framework for RL from human feedback that supports 200+ environments and includes a scalable cloud platform for collecting human preferences.