Update README

README.md

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-# 🛡️ Meta Ad-Policy RL Sandbox
-
+🛡️ Meta Ad-Policy RL Sandbox
 A custom, bleeding-edge Reinforcement Learning environment built for the Meta Ad-Policy Hackathon. This sandbox evaluates the ability of Vision-Language Models (VLMs) and LLMs to act as autonomous ad moderators, navigating complex policy violations, multimodal traps, and illegal targeting.
 
-## 🚀 Core Features
-* **OpenEnv 0.2.3 Compliant:** Fully implements the latest Meta OpenEnv specifications, including Pydantic `StepResult` state serialization and `/step` & `/reset` API endpoints.
-* **Reward Shaping:** Implements a strict `-0.05` step penalty to force the AI agent to optimize tool usage and prevent infinite analysis loops.
-* **Multimodal Traps:** Tests the limits of VLMs by presenting ads where the text is benign, but the visual elements contain severe policy violations.
-* **Containerized Infrastructure:** Fully Dockerized and highly lightweight, easily running under the 2 vCPU / 8GB RAM hackathon constraints.
-
-## 📋 Evaluation Tasks
-The environment natively supports 4 distinct adversarial tasks, loadable via the `task_id` parameter:
-1. `task_1_healthcare`: Evaluates ads for unapproved medical claims, pharmaceuticals, and subtle dog whistles.
-2. `task_2_financial`: Evaluates ads for predatory financial services, scams, and high-pressure tactics.
-3. `task_3_multimodal`: Detects policy violations hidden entirely within visual elements that bypass standard NLP text filters.
-4. `task_4_targeting`: Identifies illegal demographic targeting (e.g., adult financial services targeting minors).
+🚀 Core Features
+OpenEnv 0.2.3 Compliant: Fully implements the latest Meta OpenEnv specifications, including Pydantic StepResult state serialization and /step & /reset API endpoints.
+Reward Shaping: Implements a strict -0.05 step penalty to force the AI agent to optimize tool usage and prevent infinite analysis loops.
+Multimodal Traps: Tests the limits of VLMs by presenting ads where the text is benign, but the visual elements contain severe policy violations.
+Containerized Infrastructure: Fully Dockerized and highly lightweight, easily running under the 2 vCPU / 8GB RAM hackathon constraints.
+📋 Evaluation Tasks
+The environment natively supports 4 distinct adversarial tasks, loadable via the task_id parameter:
 
-## 🛠️ Available Agent Tools
+task_1_healthcare: Evaluates ads for unapproved medical claims, pharmaceuticals, and subtle dog whistles.
+task_2_financial: Evaluates ads for predatory financial services, scams, and high-pressure tactics.
+task_3_multimodal: Detects policy violations hidden entirely within visual elements that bypass standard NLP text filters.
+task_4_targeting: Identifies illegal demographic targeting (e.g., adult financial services targeting minors).
+🛠️ Available Agent Tools
 The environment exposes the following action space to the evaluating LLM:
-* `analyze_image`: Request VLM context for visual elements.
-* `request_landing_page`: Extract simulated URL endpoints.
-* `request_id_verification`: Check advertiser trust scores.
-* `approve` / `reject`: Terminal actions.
 
-## 🚦 Quick Start (Local)
+analyze_image: Request VLM context for visual elements.
+request_landing_page: Extract simulated URL endpoints.
+request_id_verification: Check advertiser trust scores.
+approve / reject: Terminal actions.
+🚦 Quick Start (Local)
+1. Build the Docker Image docker build -t meta-ad-sandbox .
+
+2. Run the Environment Container docker run -p 8000:8000 meta-ad-sandbox
 
-**1. Build the Docker Image**
-```bash
-docker build -t meta-ad-sandbox .
+3. Run the Automated Inference Agent Make sure your Hugging Face credentials are set, then run the evaluation script to test the agent against all 4 tasks: export HF_TOKEN="your_hugging_face_token" python inference.py