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Reorganize community environments - Move lean_proof_env, router_env, and philosophical_rlaif_env.py to environments/community/ - Add comprehensive README for community environments - This organizes community-contributed environments into a dedicated community folder for better maintainability and discoverability

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environments/lean_proof_env/README.md
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# Lean Theorem Proving Environment for Atropos
This environment allows testing Language Learning Models (LLMs) on Lean theorem proving tasks using `atroposlib`.
## Setup
1. **Atroposlib**: Ensure `atroposlib` is installed. If you are working within the main `atropos` repository, this is typically done by navigating to the `atropos` root directory (i.e., `cd LeanCopilot/atropos`) and running:
```bash
pip install -e .
```
2. **Python Dependencies**: Navigate to this environment's directory (`LeanCopilot/atropos/environments/lean_proof_env/`) and install the required Python packages using the provided `requirements.txt`:
```bash
pip install -r requirements.txt
```
This will install `datasets`, `wandb`, `tqdm`, and `python-dotenv`.
3. **OpenAI API Key**: This environment requires an OpenAI API key. You can set it in one of two ways:
* As an environment variable:
```bash
export OPENAI_API_KEY="your_actual_openai_api_key"
```
* Create a `.env` file within this directory (`LeanCopilot/atropos/environments/lean_proof_env/.env`) with the following content:
```
OPENAI_API_KEY=your_actual_openai_api_key
```
The script will automatically load this if the environment variable is not set.
4. **Lean Installation**: Ensure Lean 4 is installed and the `lean` executable is in your system's PATH. You can find installation instructions on the [Lean official website](https://leanprover.github.io/lean4/doc/setup.html).
## Running the Environment
Make sure your current working directory is this environment's directory (`LeanCopilot/atropos/environments/lean_proof_env/`).
Run the environment script using the `process` command provided by `AtroposBaseEnv`:
```bash
python lean_proof_env.py process \
--env.lean_problem_dataset_name="internal_simple_test" \
--env.total_steps=5 \
--env.group_size=1 \
--env.wandb_name="lean_simple_test_run" \
--openai.model_name="gpt-4o"
```
### Command-Line Arguments:
* `--env.lean_problem_dataset_name`:
* `"internal_simple_test"`: Uses a small set of hardcoded simple theorems (defined within the script).
* `"Tonic/MiniF2F"`: Attempts to load the MiniF2F benchmark from Hugging Face datasets. You might need to specify `--env.lean_problem_dataset_split` (e.g., "test" or "train").
* `--env.total_steps`: Number of problems (or items) to process. The `internal_simple_test` set has 8 problems; if `total_steps` is less, a random subset will be chosen for that many steps.
* `--env.group_size`: Number of LLM attempts per problem.
* `--env.wandb_name`: Name for the WandB run if `use_wandb` is enabled (e.g., by passing `--env.use_wandb=True`).
* `--openai.model_name`: The OpenAI model to use (e.g., "gpt-4o", "gpt-4-turbo", "gpt-3.5-turbo"). This overrides the default in `config_init`.
* `--openai.api_key`: Can be passed explicitly, but using the environment variable or `.env` file is recommended and more secure.
* `--openai.base_url`: If using a custom OpenAI-compatible API endpoint.
The script will generate a `.jsonl` file with the trajectories and an HTML visualization of the rollouts. By default, these are saved in a `data/` subdirectory created within the current working directory (e.g., `LeanCopilot/atropos/environments/lean_proof_env/data/`).
## Customization
* **Problem Sets**: Modify the `setup()` method in `lean_proof_env.py` to load different Lean problems or datasets.
* **LLM Prompts**: Adjust the `get_next_item()` method in `lean_proof_env.py` to change the prompts sent to the LLM.
* **Configuration**: Default configurations are defined in the `LeanProofEnvConfig` class within `lean_proof_env.py`. Most of these can be overridden by command-line arguments (e.g., `--env.max_proof_generation_tokens=256`).