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Can Large Language Models Independently Complete Tasks? A Dynamic Evaluation Framework for Multi-turn Task Planning and Completion
Large language models (LLMs) are increasingly relied upon for multi-turn dialogue to conduct complex tasks. However, existing …
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ReDMan: Reliable Dexterous Manipulation with Safe Reinforcement Learning
Dexterous hand manipulation is a crucial ability for robots in various applications. However, ensuring safety and reliability during …
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Med-Aligner Empowers LLM Medical Applications for complex medical scenarios
Large language models (LLMs) show great promise in medical applications, but challenges like limited high-quality data, closed-source …
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Large Language Models in Medicine: Applications, Challenges, and Future Directions
In recent years, large language models (LLMs) represented by GPT-4 have developed rapidly and performed well in various natural …
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TIMAR: Transition-Informed Representation for Sample-Efficient Multi-agent Reinforcement Learning
In MARL (Multi-Agent Reinforcement Learning), the trial-and-error learning paradigm based on multiple agents requires massive …
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Discrete Information Acquisition in Financial Markets
We study investors’ information acquisition strategies under arbitrary and discrete sets of information precision and derive conditions …
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Transforming the Synthesis of Carbon Nanotubes with Machine Learning Models and Automation
Carbon-based nanomaterials (CBNs) hold immense promise in electronics, energy, and mechanics. However, their practical applications …
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DSR: Reinforcement Learning with Dynamical Skill Refinement
Skill-based reinforcement learning (Skill-based RL) is an efficient paradigm for solving sparse-reward tasks by extracting skills from …
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Adaptive Pessimism via Target Q-Value for Offline Reinforcement Learning
Offline reinforcement learning (RL) methods learn from datasets without further environment interaction, facing errors due to …
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Multi-Agent Deep Reinforcement Learning for Multi-Echelon Inventory Management
We apply heterogeneous-agent proximal policy optimization (HAPPO), a multi-agent deep reinforcement learning (MADRL) algorithm, to the …
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