Reason for future, act for now: A principled architecture for autonomous llm agents
Large language models (LLMs) demonstrate impressive reasoning abilities, but translating
reasoning into actions in the real world remains challenging. In particular, it is unclear how …
reasoning into actions in the real world remains challenging. In particular, it is unclear how …
A systematic survey on large language models for algorithm design
Algorithm Design (AD) is crucial for effective problem-solving across various domains. The
advent of Large Language Models (LLMs) has notably enhanced the automation and …
advent of Large Language Models (LLMs) has notably enhanced the automation and …
Efficient Reinforcement Learning with Large Language Model Priors
In sequential decision-making (SDM) tasks, methods like reinforcement learning (RL) and
heuristic search have made notable advances in specific cases. However, they often require …
heuristic search have made notable advances in specific cases. However, they often require …
Cross-Domain Integration for General Sensor Data Synthesis: Leveraging LLMs and Domain-Specific Generative Models in Collaborative Environments
Synthetic data has emerged as a critical component in the fields of machine learning and
data science, providing a solution to overcome limitations associated with real-world data …
data science, providing a solution to overcome limitations associated with real-world data …
Language-Model-Assisted Bi-Level Programming for Reward Learning from Internet Videos
Learning from Demonstrations, particularly from biological experts like humans and animals,
often encounters significant data acquisition challenges. While recent approaches leverage …
often encounters significant data acquisition challenges. While recent approaches leverage …
Monte Carlo Planning with Large Language Model for Text-Based Games
Text-based games provide valuable environments for language-based autonomous agents.
However, planning-then-learning paradigms, such as those combining Monte Carlo Tree …
However, planning-then-learning paradigms, such as those combining Monte Carlo Tree …
Deep reinforcement learning for traffic light control optimization in multi-modal simulation of SUMO
Y XU - 2024 - repository.tudelft.nl
This research investigates the application of different deep reinforcement learning methods
for optimizing traffic light control in multi-modal urban traffic environments using the SUMO …
for optimizing traffic light control in multi-modal urban traffic environments using the SUMO …