Context shift reduction for offline meta-reinforcement learning
Offline meta-reinforcement learning (OMRL) utilizes pre-collected offline datasets to
enhance the agent's generalization ability on unseen tasks. However, the context shift …
enhance the agent's generalization ability on unseen tasks. However, the context shift …
Contrastive modules with temporal attention for multi-task reinforcement learning
In the field of multi-task reinforcement learning, the modular principle, which involves
specializing functionalities into different modules and combining them appropriately, has …
specializing functionalities into different modules and combining them appropriately, has …
Online prototype alignment for few-shot policy transfer
Abstract Domain adaptation in RL mainly deals with the changes of observation when
transferring the policy to a new environment. Many traditional approaches of domain …
transferring the policy to a new environment. Many traditional approaches of domain …
Hypothesis, Verification, and Induction: Grounding Large Language Models with Self-Driven Skill Learning
Large language models (LLMs) show their powerful automatic reasoning and planning
capability with a wealth of semantic knowledge about the human world. However, the …
capability with a wealth of semantic knowledge about the human world. However, the …
LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions
In recent years, Large Language Models (LLMs) have shown great abilities in various tasks,
including question answering, arithmetic problem solving, and poem writing, among others …
including question answering, arithmetic problem solving, and poem writing, among others …
Advancing DRL Agents in Commercial Fighting Games: Training, Integration, and Agent-Human Alignment
Deep Reinforcement Learning (DRL) agents have demonstrated impressive success in a
wide range of game genres. However, existing research primarily focuses on optimizing …
wide range of game genres. However, existing research primarily focuses on optimizing …
Combined Text-Visual Attention Models for Robot Task Learning and Execution
In this work, we explore the interplay between text and visual attention mechanisms in a
robot reinforcement learning setting, where robotic tasks are conveyed through natural …
robot reinforcement learning setting, where robotic tasks are conveyed through natural …
for Robot Task Learning and Execution
In this work, we explore the interplay between text and visual attention mechanisms in a
robot reinforcement learning setting, where robotic tasks are conveyed through natural …
robot reinforcement learning setting, where robotic tasks are conveyed through natural …