| title | Lazarus Task Env | |
|---|---|---|
| emoji | 🚀 | |
| colorFrom | blue | |
| colorTo | green | |
| sdk | docker | |
| app_port | 7860 | |
| tags |
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Pricing is a critical task in every business. Finding the optimal price that balances sales volume and profit margin while reacting to competitors and inventory constraints is a complex challenge humans face daily. PriceSim provides a realistic simulation of this task for AI agents to learn and evaluate their strategic decision-making capabilities.
The agent provides a single continuous value:
price(float): The price to set for the product in the current step (Range: 0.0 to 1000.0).
The agent receives a dictionary of values:
current_price(float): The price set by the agent in the previous step.competitor_price(float): The current price of the main competitor.inventory(float): The number of units remaining in stock.demand_signal(float): A signal indicating market demand strength (1.0 = normal).step(int): The current step in the episode (0 to 30).
- Easy: Monopoly scenario. Constant demand, no competitor reaction. Goal: Find the static optimal price.
- Medium: Competitive market. A competitor reacts to your price changes. Goal: Maintain market share while preserving margins.
- Hard: Dynamic environment. Seasonal demand fluctuations, aggressive competitor, and limited inventory. Goal: Strategic long-term inventory clearance at maximum profit.
The reward is provided at each step and is calculated as:
reward = max(0, profit / 200)
Where profit = sales * (price - cost). This provides a partial progress signal throughout the trajectory.
Each task has a deterministic grader that calculates a final score (0.0 to 1.0) based on the total profit achieved compared to a theoretical maximum for that task.
- Success is defined as achieving a score >= 0.7.
- Build:
docker build -t pricesim . - Run:
docker run -p 3000:3000 pricesim - Inference:
python3 inference.py(RequiresOPENAI_API_KEY,API_BASE_URL,MODEL_NAME)
- Easy: 0.95
- Medium: 0.82
- Hard: 0.65