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Create openenv.yaml
Browse files- openenv.yaml +56 -0
openenv.yaml
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# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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# openenv.yaml β Disaster Grid Environment Metadata
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# Conforms to the OpenEnv 1.0 specification
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# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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name: disaster-grid-env
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version: "1.0.0"
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description: >
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A 5x5 fully-observable grid simulation where an RL agent (Llama-3 trained via GRPO)
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acts as an Autonomous Emergency Manager. The agent must manage its energy
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to navigate and repair decaying city sectors before critical failure.
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author: "Sanjith & Varsha"
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license: MIT
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# ββ interface βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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interface:
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# This tells the framework exactly where our Python class lives!
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step: "src.disaster_grid.environment.CityGrid.step"
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reset: "src.disaster_grid.environment.CityGrid.reset"
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# ββ observation space (From our models.py) ββββββββββββββββββββββββββββββββββββ
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observation_space:
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type: object
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fields:
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step_number: {type: integer}
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agent_position: {type: integer, description: "Index 0-24"}
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agent_energy: {type: integer, description: "Scale 0-100"}
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current_sector_health: {type: integer}
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critical_sectors:
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type: array
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items: {type: integer}
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average_city_health: {type: number, format: float}
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# ββ action space (From our models.py) βββββββββββββββββββββββββββββββββββββββββ
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action_space:
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type: object
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fields:
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action: {type: string, enum: [MOVE_N, MOVE_S, MOVE_E, MOVE_W, REPAIR, RECHARGE, WAIT]}
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reasoning: {type: string, description: "Chain of thought explanation"}
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# ββ runtime βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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runtime:
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python: "3.10"
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entrypoint: "app.py"
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port: 7860
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framework: fastapi
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# ββ tags ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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tags:
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- disaster-management
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- grid-world
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- reinforcement-learning
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- grpo
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- llama-3
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- huggingface-spaces
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