mtrxk (Solo)

Adaptive Navigation OpenEnv is a partially observable exploration environment designed for training and evaluating LLM agents on multi-step planning tasks. The agent operates in a 2D world where it only sees a small local observation window and must navigate efficiently under energy constraints. The objective is to collect a key, unlock a checkpoint, and reach a goal while reasoning over incomplete information. The environment includes dynamic obstacles, mission tracking, and reward feedback, making it suitable for reinforcement learning and agentic decision-making research. The project demonstrates a full pipeline including an interactive Streamlit environment, OpenEnv deployment on Hugging Face Spaces, and a minimal HF TRL training scaffold in Colab. This environment provides a compact benchmark for studying autonomous exploration, navigation, and planning with language model agents.