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Add HF Space metadata

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  1. README.md +31 -20
README.md CHANGED
@@ -1,3 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
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  # GridMind-RL
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  **Industrial building energy management reinforcement learning environment**
@@ -34,11 +45,11 @@ GridMind-RL consists of three tightly integrated components:
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  Agent (python/inference.py)
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  ?? HTTP POST /step, /reset, /grade
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  ?
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- Go Environment Server (main.go) � Port 7860
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  ?
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  Physics Engine (env/environment.go) + Rewards (env/rewards.go) + Tasks (env/tasks.go)
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  ?
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- Web Dashboard (dashboard/server.py) � Port 7861
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  ```
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  **Design philosophy:**
@@ -55,7 +66,7 @@ Web Dashboard (dashboard/server.py)
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  | Field | Type | Range | Description |
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  |-------|------|-------|-------------|
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- | `indoor_temperature` | float | [15-27] �C | Building indoor temperature |
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  | `thermal_storage_level` | float | [0-1] | Thermal storage charge (0=empty, 1=full) |
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  | `process_demand` | float | [5-50] kW | Baseline demand |
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  | `current_price` | float | [0.03-0.25] $/kWh | Electricity price |
@@ -82,7 +93,7 @@ Web Dashboard (dashboard/server.py)
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  | Component | Description |
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  |-----------|-------------|
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  | **Cost Savings** | Negative cost per energy consumed |
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- | **Temperature Constraint** | Penalty if T outside [19-23]�C |
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  | **Grid Response** | Bonus for load shedding during stress |
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  | **Deadline Penalty** | Penalty for missed batch deadlines |
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  | **Efficiency Bonus** | Bonus for off-peak charging |
@@ -100,7 +111,7 @@ Web Dashboard (dashboard/server.py)
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  | Task 3 | Hard | Full demand response + scheduling | **0.598** |
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  **Task 1 (Easy)**: Cost minimization, no constraints
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- **Task 2 (Medium)**: Cost + temperature comfort (19-23�C)
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  **Task 3 (Hard)**: Cost + comfort + grid response + batch scheduling + carbon
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  ---
@@ -138,11 +149,11 @@ python inference.py --episodes 1
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  | Variable | Required | Default | Description |
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  |----------|----------|---------|-------------|
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- | `HF_TOKEN` | Yes | � | LLM API key |
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  | `API_BASE_URL` | No | `https://openrouter.ai/api/v1` | LLM endpoint |
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  | `MODEL_NAME` | No | `meta-llama/llama-3.3-70b-instruct:free` | Model ID |
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  | `ENV_URL` | No | `http://localhost:7860` | Environment server URL |
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- | `OPENAI_API_KEY` | No | � | Alternative to HF_TOKEN |
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  ---
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@@ -187,23 +198,23 @@ gridmind-rl/
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  +-- openenv.yaml # OpenEnv spec
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  +-- Dockerfile # Container build
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  +-- env/
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- � +-- environment.go # Physics simulation
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- � +-- models.go # Data models
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- � +-- rewards.go # Reward computation
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- � +-- tasks.go # Task grading
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  +-- python/
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- � +-- inference.py # LLM agent
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- � +-- models.py # Pydantic models
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- � +-- requirements.txt
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  +-- dashboard/
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- � +-- server.py # Web server (port 7861)
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- � +-- static/ # Frontend assets
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  +-- data/
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- � +-- price_curves.json # Price data
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- � +-- generate_prices.py # Price generator
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  +-- tests/
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- � +-- test_graders.py # Python tests
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- � +-- environment_test.go # Go tests
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  +-- baseline_scores.json # Reference scores
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  +-- .env.example # Environment template
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  +-- LICENSE # MIT License
 
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+ ---
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+ title: GridMind-RL
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+ emoji: ⚡
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+ colorFrom: green
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+ colorTo: blue
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+ sdk: docker
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+ app_port: 7860
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+ pinned: false
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+ license: mit
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+ ---
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+
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  # GridMind-RL
13
 
14
  **Industrial building energy management reinforcement learning environment**
 
45
  Agent (python/inference.py)
46
  ?? HTTP POST /step, /reset, /grade
47
  ?
48
+ Go Environment Server (main.go) � Port 7860
49
  ?
50
  Physics Engine (env/environment.go) + Rewards (env/rewards.go) + Tasks (env/tasks.go)
51
  ?
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+ Web Dashboard (dashboard/server.py) � Port 7861
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  ```
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55
  **Design philosophy:**
 
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  | Field | Type | Range | Description |
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  |-------|------|-------|-------------|
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+ | `indoor_temperature` | float | [15-27] �C | Building indoor temperature |
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  | `thermal_storage_level` | float | [0-1] | Thermal storage charge (0=empty, 1=full) |
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  | `process_demand` | float | [5-50] kW | Baseline demand |
72
  | `current_price` | float | [0.03-0.25] $/kWh | Electricity price |
 
93
  | Component | Description |
94
  |-----------|-------------|
95
  | **Cost Savings** | Negative cost per energy consumed |
96
+ | **Temperature Constraint** | Penalty if T outside [19-23]�C |
97
  | **Grid Response** | Bonus for load shedding during stress |
98
  | **Deadline Penalty** | Penalty for missed batch deadlines |
99
  | **Efficiency Bonus** | Bonus for off-peak charging |
 
111
  | Task 3 | Hard | Full demand response + scheduling | **0.598** |
112
 
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  **Task 1 (Easy)**: Cost minimization, no constraints
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+ **Task 2 (Medium)**: Cost + temperature comfort (19-23�C)
115
  **Task 3 (Hard)**: Cost + comfort + grid response + batch scheduling + carbon
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  ---
 
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  | Variable | Required | Default | Description |
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  |----------|----------|---------|-------------|
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+ | `HF_TOKEN` | Yes | � | LLM API key |
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  | `API_BASE_URL` | No | `https://openrouter.ai/api/v1` | LLM endpoint |
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  | `MODEL_NAME` | No | `meta-llama/llama-3.3-70b-instruct:free` | Model ID |
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  | `ENV_URL` | No | `http://localhost:7860` | Environment server URL |
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+ | `OPENAI_API_KEY` | No | � | Alternative to HF_TOKEN |
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  ---
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  +-- openenv.yaml # OpenEnv spec
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  +-- Dockerfile # Container build
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  +-- env/
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+ � +-- environment.go # Physics simulation
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+ � +-- models.go # Data models
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+ � +-- rewards.go # Reward computation
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+ � +-- tasks.go # Task grading
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  +-- python/
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+ � +-- inference.py # LLM agent
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+ � +-- models.py # Pydantic models
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+ � +-- requirements.txt
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  +-- dashboard/
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+ � +-- server.py # Web server (port 7861)
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+ � +-- static/ # Frontend assets
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  +-- data/
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+ � +-- price_curves.json # Price data
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+ � +-- generate_prices.py # Price generator
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  +-- tests/
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+ � +-- test_graders.py # Python tests
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+ � +-- environment_test.go # Go tests
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  +-- baseline_scores.json # Reference scores
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  +-- .env.example # Environment template
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  +-- LICENSE # MIT License