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| title: Autonomy Calibration Hub |
| colorFrom: indigo |
| colorTo: blue |
| sdk: docker |
| pinned: false |
| app_port: 7860 |
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| # Epistemic Agency Hub: Autonomy Calibration Environment |
| ### 🏆 OpenEnv India Hackathon 2026 Official Submission |
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| The **Epistemic Agency Hub** is a specialized reinforcement learning benchmark designed to evaluate an agent's ability to manage uncertainty through **Calibrated Autonomy**. |
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| Unlike traditional RL agents that only optimize for task execution, our environment mandates "Epistemic Actions"—specifically the `INVESTIGATE` behavior—where an agent must resolve informational gaps before committing to high-stakes decisions. |
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| ## 🏗️ Core Framework: Investigate-then-Act |
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| The environment implements a **calibration-first workflow** to reduce agential over-confidence: |
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| 1. **Uncertainty Identification**: The agent receives a state with ambiguous or incomplete data. |
| 2. **Epistemic Phase**: The agent must decide whether to `INVESTIGATE` (resolving uncertainty at a cost) or `ACT` (committing to a decision). |
| 3. **Calibrated Action**: Success is measured by the ability to minimize investigation costs while maximizing decision accuracy. |
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| ## 🛠️ Technical Implementation |
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| ### 🧠 Action Space & Behavior |
| - **OpenEnv Compliance**: Fully compliant with the latest OpenEnv API specifications. |
| - **Action Set**: |
| - `INVESTIGATE`: Queries the internal knowledge base to reduce state entropy. |
| - `ACT`: Executes the final decision based on the current belief state. |
| - `RECOVER`: Error-handling mechanism for miscalibrated decisions. |
| - **State Management**: Transient state variables track confidence levels and informational completeness throughout the trajectory. |
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| ### ⚖️ Reward Model (GRPO) |
| We utilize **Group Relative Policy Optimization (GRPO)** to calibrate the agent's logic: |
| - **Causal Merit Reward**: Distributed for successful investigation steps leading to high accuracy. |
| - **Calibration Penalty**: High penalties for "over-confident" actions taken during high uncertainty. |
| - **Efficiency Bonus**: Incentivizes reaching a confident state with the minimum number of steps. |
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| ## 📈 Performance Evidence & Metrics |
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| Our trained agent demonstrates clear convergence during the GRPO calibration phase. |
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| | Metric | Baseline | Calibrated Agent (v2) | Improvement | |
| | :------------------------- | :------- | :-------------------- | :---------- | |
| | **Epistemic Success Rate** | 64% | **92%** | +28% | |
| | **Avg. Reward** | 0.42 | **0.87** | +107% | |
| | **Risk Incidents** | 12 | **2** | -83% | |
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| ## 🏆 Submission Artifacts |
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| - **Hugging Face Space**: [Live Benchmark Hub](https://huggingface.co/spaces/JOY0021/autonomy-calibration-benchmark) |
| - **Trained Weights**: [autonomy-agent-v2](https://huggingface.co/JOY0021/autonomy-agent-v2) |
| - **Documentation**: |
| - 📖 [Technical Case Study (Blog)](Blog.md) |
| - 🚀 [Step-by-Step Walkthrough](WALKTHROUGH.md) |
| - **Reproducibility**: [](https://colab.research.google.com/github/Rhythm280/Autonomy-Calibration-Environment/blob/main/notebooks/training.ipynb) |
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| ## 🚀 Deployment and Setup |
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| ### Local Development |
| ```bash |
| # Install dependencies |
| pip install -r requirements.txt |
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| # Start the dashboard |
| uvicorn main:app --port 7860 |
| ``` |
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| ### Production Build (Docker) |
| ```bash |
| docker build -t autonomy-calibration-hub . |
| docker run -p 7860:7860 autonomy-calibration-hub |
| ``` |
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| MIT License - OpenEnv India 2026. |
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