Path-finding-agent-using-LLM-and-RL
What it is
A research project implementing an agent that navigates 1D and 2D homogeneous environments using a combination of Reinforcement Learning (RL) and Large Language Models (LLMs). The agent leverages LLM supervision to interpret environmental clues to identify optimal routes more efficiently.
The problem it solves
Standard RL pathfinding can be slow to converge or lack high-level reasoning; this approach uses LLMs to provide 'clues' or guidance to speed up the process.
What you could use it for
- Pathfinding in grid-based environments
- Robotic path planning with high-level reasoning
- Navigation tasks in homogeneous 1D/2D spaces
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.