resolved conflict

environments/community/README.md

@@ -1,54 +1,30 @@
 # Community Environments
 
-This directory contains environments contributed by the community. These environments demonstrate various use cases and extensions of the Atropos framework.
+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.
 
-## Available Environments
+**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.
 
-### 1. Lean Proof Environment (`lean_proof_env/`)
-**Author**: GabinFay
-**Purpose**: Testing Language Learning Models (LLMs) on Lean theorem proving tasks
+## Contributing Your Environment
 
-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
+We encourage you to contribute your own RL environments! When developing a new environment, please follow these guidelines:
 
-**Requirements**: Lean 4 installation, OpenAI API key
+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.
 
-### 2. Router Environment (`router_env/`)
-**Author**: GabinFay
-**Purpose**: Multi-agent routing and coordination system
+### Environment Standards
 
-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
+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
 
-**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
-**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
-
-## Contributing
+### Submission Process
 
 To contribute a new environment to the community collection:
 
@@ -64,14 +40,57 @@ To contribute a new environment to the community collection:
 5. **Test thoroughly** before submitting
 6. **Submit a pull request** with a clear description
 
-## Environment Standards
+Once your environment is ready, please follow the guidelines in our main [CONTRIBUTING.md](../../../CONTRIBUTING.md) to submit your contribution.
 
-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
+---
+
+## 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
+
+---
 
 ## Support
 
@@ -80,6 +99,4 @@ For questions or issues with community environments:
 - 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.*