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environments/community/README.md

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+# Community Environments
+
+This directory is home to community-contributed training environments for Atropos. Environments submitted by the community will be placed here after an initial code review.
+
+**Note:** Environments in this directory are pending full testing and integration. While they have passed a basic code check, they may not yet have been rigorously validated on our compute cluster.
+
+## Contributing Your Environment
+
+We encourage you to contribute your own RL environments! When developing a new environment, please follow these guidelines:
+
+1. **Create your environment in this `environments/community/` subdirectory.** This helps us keep new submissions organized.
+2. **Preferred Import Style:** We prefer that you treat your environment's directory as the package root for imports within your environment code. For example, if you need to import `SomeClass`, you can do so directly:
+   ```python
+   from some_file_in_my_env import SomeClass
+   ```
+   This helps maintain consistency and makes it easier to integrate your environment.
+
+### Environment Standards
+
+Community environments should:
+- Include clear documentation and setup instructions
+- Specify all dependencies in requirements files
+- Provide example configurations and usage
+- Follow the AtroposBaseEnv pattern for consistency
+- Include appropriate error handling and validation
+
+### Submission Process
+
+To contribute a new environment to the community collection:
+
+1. **Fork the repository** and create a new branch
+2. **Add your environment** to this `community/` directory
+3. **Include comprehensive documentation**:
+   - README with setup instructions
+   - Requirements file for dependencies
+   - Example usage and configuration
+4. **Follow naming conventions**:
+   - Use descriptive directory names for complex environments
+   - Single file environments should have descriptive names
+5. **Test thoroughly** before submitting
+6. **Submit a pull request** with a clear description
+
+Once your environment is ready, please follow the guidelines in our main [CONTRIBUTING.md](../../../CONTRIBUTING.md) to submit your contribution.
+
+---
+
+## Available Environments
+
+### 1. Lean Proof Environment (`lean_proof_env/`)
+**Author**: [GabinFay](https://github.com/GabinFay)
+**Purpose**: Testing Language Learning Models (LLMs) on Lean theorem proving tasks
+
+A comprehensive environment for evaluating LLMs on formal mathematical reasoning using the Lean theorem prover. Features include:
+- Support for custom problem datasets or MiniF2F benchmark
+- Integration with Lean 4 theorem prover
+- Configurable difficulty levels and problem sets
+- Automated proof validation
+
+**Requirements**: Lean 4 installation, OpenAI API key
+
+### 2. Router Environment (`router_env/`)
+**Author**: [GabinFay](https://github.com/GabinFay)
+**Purpose**: Multi-agent routing and coordination system
+
+A sophisticated environment for testing agent routing and coordination capabilities. Includes:
+- Multiple specialized agents (calendar, contact, Gmail, telephony, etc.)
+- Model Contextualized Protocol (MCP) tools integration
+- Spotify, Google Maps, and Perplexity integrations
+- Complex multi-turn conversation handling
+
+**Features**:
+- Telephony agent with inbound/outbound call handling
+- Calendar and contact management
+- Memory and calculation agents
+- Router agent for intelligent task delegation
+
+### 3. Philosophical RLAIF Environment (`philosophical_rlaif_env.py`)
+**Author**: [GabinFay](https://github.com/GabinFay)
+**Purpose**: Reinforcement Learning from AI Feedback (RLAIF) for philosophical reasoning
+
+An environment focused on training models for deep philosophical inquiry and reasoning. Features:
+- Deep thinking prompts with systematic reasoning processes
+- Preference learning for philosophical depth and nuance
+- Multi-perspective analysis and assumption questioning
+- Evaluation of response quality for philosophical discussions
+
+**Capabilities**:
+- Generates paired responses for preference comparison
+- Uses judge models to evaluate philosophical depth
+- Tracks preference consistency and reasoning quality
+- Supports WandB logging for training insights
+
+### 4. Playwright Agent Environment (`playwright_agent_env.py`)
+**Author**: [erikqu](https://github.com/erikqu)
+**Purpose**: Web automation and browser interaction for LLM agents
+
+A comprehensive environment for training LLMs to interact with web pages through browser automation. Features:
+- Playwright-based browser control with headless operation
+- Screenshot-based visual input for LLM decision making
+- JSON-based action commands (navigate, click, type, finish)
+- Video recording of browser sessions for evaluation
+- Google Gemini integration for success evaluation
+
+**Capabilities**:
+- Loads tasks from WebVoyager dataset or custom task definitions
+- Supports development mode for testing without LLM calls
+- Automatic reward computation based on success and efficiency
+- Comprehensive error handling and fallback mechanisms
+- Integration with Atropos training pipeline
+
+**Requirements**: Playwright, optional Google Gemini API for evaluation
+
+### 5. Metric Card Generator Environment (`metric_card_generator/`)
+**Author**: [vivek100](https://github.com/vivek100)
+**Purpose**: Structured JSON generation for AI model evaluation dashboards
+
+A comprehensive environment for training LLMs to generate well-structured JSON configurations for Metric Card UI components. Features:
+- Closed-loop generation, evaluation, and visualization pipeline
+- Schema validation for JSON metric card configurations
+- Multi-dimensional evaluation (validity, compliance, semantic quality)
+- Support for various business domains and metric types
+- WandB integration for performance tracking
+
+**Capabilities**:
+- Generates metric cards for diverse business contexts (e-commerce, finance, healthcare, etc.)
+- Validates JSON structure against predefined schemas
+- Evaluates semantic quality and formatting consistency
+- Provides training data extraction and filtering utilities
+- Includes visualization tools for score distribution analysis
+
+**Components**:
+- `metric_card_generator.py`: Main environment implementation
+- `extract_metric_training.py`: Training data extraction utility
+- `trainingDataScript.py`: Dataset creation from collected examples
+- `show_score_distribution.py`: Performance analysis visualization
+
+**Requirements**: Pydantic, tqdm
+
+---
+
+## Support
+
+For questions or issues with community environments:
+- Check the individual environment's README first
+- Open an issue in the main repository
+- Tag the environment author if possible
+
+*These environments are community contributions and may have different maintenance levels and support compared to core Atropos environments.*