The approach of Reinforcement Learning from Human Feedback (RLHF) is widely used for
enhancing pre-trained Language Models (LM), enabling them to better align with human …

Direct Preference Optimization (DPO) improves the alignment of large language models
(LLMs) with human values by training directly on human preference datasets, eliminating the …

Continual Imitation Learning (CiL) involves extracting and accumulating task knowledge
from demonstrations across multiple stages and tasks to achieve a multi-task policy. With …

Continual reinforcement learning (CRL) empowers RL agents with the ability to learn from a
sequence of tasks, preserving previous knowledge and leveraging it to facilitate future …

Domain Randomization (DR) is commonly used for sim2real transfer of reinforcement
learning (RL) policies in robotics. Most DR approaches require a simulator with a fixed set of …

In continual learning from demonstration (CLfD), a robot learns a sequence of real-world
motion skills continually from human demonstrations. Recently, hypernetworks have been …

Exploring the state space efficiently is a crucial problem in reinforcement learning as it holds
significant importance for learning optimal policies. One effective approach involves learning …

Continual Reinforcement Learning (CRL) is a powerful tool that enables agents to learn a
sequence of tasks, accumulating knowledge learned in the past and using it for …

In continual learning from demonstration (CLfD), a robot learns a sequence of real-world
motion skills continually from human demonstrations. Recently, hypernetworks have been …