"Can an AI learn the delicate balance between profit and sustainability in a changing climate?"
KisanAgent is an advanced Reinforcement Learning (RL) agent built on the OpenEnv Framework for the Meta-PyTorch Hackathon Finale. It solves the "KisanEnv" challenge: a high-fidelity farming simulation where every decision—from irrigation to pest control—carries causal consequences over a 90-day growth cycle.
🚀 The Vision: Why KisanAgent?
Global agriculture faces a dual crisis: Climate Volatility and Economic Uncertainty. Modern farmers must navigate complex, non-linear variables like soil moisture, pest cycles, and market prices.
KisanAgent is a proof-of-concept demonstrating that Deep Reinforcement Learning (GRPO) can master these complexities. By training a Large Language Model (LLM) as a decision-making agent, we bridge the gap between "Stochastic Simulation" and "Human-Readable Reasoning."
🧠 The Novel Environment: KisanEnv
Built strictly on the OpenEnv Core, KisanEnv is a specialized Gymnasium-based world that features:
