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Found 6 papers, 4 papers with code
LLM-Personalize: Aligning LLM Planners with Human Preferences via Reinforced Self-Training for Housekeeping Robots
We introduce LLM-Personalize, a novel framework with an optimization pipeline designed to personalize LLM planners for household robotics.
Planning to Go Out-of-Distribution in Offline-to-Online Reinforcement Learning
Offline pretraining with a static dataset followed by online fine-tuning (offline-to-online, or OtO) is a paradigm well matched to a real-world RL deployment process.
Deep Reinforcement Learning for Multi-Agent Interaction
The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning.
Temporal Disentanglement of Representations for Improved Generalisation in Reinforcement Learning
Reinforcement Learning (RL) agents are often unable to generalise well to environment variations in the state space that were not observed during training.
Multi-Horizon Representations with Hierarchical Forward Models for Reinforcement Learning
Learning control from pixels is difficult for reinforcement learning (RL) agents because representation learning and policy learning are intertwined.
Learning Temporally-Consistent Representations for Data-Efficient Reinforcement Learning
Deep reinforcement learning (RL) agents that exist in high-dimensional state spaces, such as those composed of images, have interconnected learning burdens.
