Inspiration
In dynamic real-world economies, coordinating independent autonomous actors while navigating fluctuating commodity markets is a fundamental challenge in artificial intelligence and operations research. We wanted to build an intelligent, fully autonomous economic agent capable of managing multi-agent labor logistics, agricultural yields, livestock care schedules, and competitive market timing without human intervention.
What it does
AgriMind AI commands an entire agricultural enterprise across a 30-day (720 simulation hours) timeline:
- Autonomous Multi-Agent Routing: Simultaneously coordinates a Farmer and up to 12 hired hands with collision-free pathfinding across a 10x10 spatial grid.
- Fibonacci Labor Scheduling: Employs mathematical marginal utility modeling to evaluate whether hiring the next worker (whose wage scales by Fibonacci numbers $1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377$) will produce a net economic surplus before hiring.
- Order-Book Market Arbitrage: Dynamically monitors Town Shop consumption cycles and town inventory supply elasticity to execute produce sales during price rebounds rather than dumping goods into saturated markets.
- Counterfactual RL Meta-Manager: Uses an embedded decision policy evaluating paired states to safely assess late-stage workforce expansion under volatile market conditions.
How we built it
- Built on Python and the Kaggle Environments simulation engine (
kaggriculture). - Developed a custom spatial Manhattan/Graph-search pathfinding system to route workers between crop plots, pastures, sheds, and wells with zero deadlock.
- Implemented microeconomic order-book simulation to track price elasticity curves and rolling baseline floors for 8 distinct commodities.
- Tested across 100 procedurally generated random seeds to benchmark reliability, achieving a peak single-match score of 128,783 coins and an average of >90,000 coins (compared to ~0 for baseline starters).
- Created a 1-command interactive HTML replay runner (
run_demo.py --open) allowing anyone to inspect the match frame-by-frame in their web browser.
Challenges we ran into
- Shared Town Market Saturation: When competing agents flood commodities into the market, prices plummet. Our agent initially held onto inventory for too long; we solved this by designing dynamic price-floor elasticity that adapts sale thresholds when shed capacity builds up.
- Worker Traffic Congestion: Coordinating up to 14 workers in a tight 10x10 grid with only 4 shed access points caused movement deadlocks. We resolved this by assigning dedicated livestock specialists and routing produce ferrying early in the day.
- Exponential Labor Costs: Worker hiring costs escalate rapidly following the Fibonacci sequence. We developed a marginal productivity formula that calculates expected future return vs. hiring costs over remaining days.
Accomplishments that we're proud of
- Achieving over 100,000+ coins consistently from a starting capital of only 1,000 coins.
- Reaching a peak score of 128,783 coins on random procedural seeds.
- Achieving >88% active labor efficiency, ensuring workers spend minimal turns idling or passing.
- Building a 100% self-contained Python architecture that runs cleanly and generates interactive visual replays in one click.
What we learned
- How microeconomic order-book dynamics directly influence multi-agent scheduling.
- Techniques for safe offline reinforcement learning and counterfactual evaluation in complex, multi-turn environments.
- The power of spatial graph heuristics in coordinating multi-robot teams in constrained grid environments.
What's next for AgriMind AI
- Extending the market predictor to model opponent inventory forecasting using non-cooperative game theory.
- Implementing Monte Carlo Tree Search (MCTS) for long-horizon multi-day crop rotation planning.
- Adapting the core logistics engine to real-world warehouse automation and agricultural supply chain simulators.
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