Quick Start
===========

Get up and running with Game Reasoning Arena in minutes.

Installation
------------

.. code-block:: bash

   git clone https://github.com/SLAMPAI/game_reasoning_arena.git
   cd game_reasoning_arena
   conda env create -f environment.yaml
   conda activate game_reasoning_arena
   pip install -e .

Your First Game
---------------

Run a simple game using the command-line interface:

.. code-block:: bash

   # Run a Tic-Tac-Toe game with random agents
   python scripts/runner.py --config src/game_reasoning_arena/configs/example_config.yaml --override \
     env_configs.0.game_name=tic_tac_toe \
     agents.player_0.type=random \
     agents.player_1.type=random \
     num_episodes=1

.. code-block:: bash

   # Run a Connect Four game
   python scripts/runner.py --config src/game_reasoning_arena/configs/example_config.yaml --override \
     env_configs.0.game_name=connect_four \
     agents.player_0.type=random \
     agents.player_1.type=random \
     num_episodes=1

LLM vs Random Agent
-------------------

Try an LLM agent against a random player:

.. code-block:: bash

   python scripts/runner.py --config src/game_reasoning_arena/configs/example_config.yaml --override \
     env_configs.0.game_name=kuhn_poker \
     agents.player_0.type=llm \
     agents.player_0.model=litellm_groq/llama3-8b-8192 \
     agents.player_1.type=random \
     num_episodes=5

Parallel Execution with Ray
---------------------------

For faster experiments, enable Ray parallelization:

.. code-block:: bash

   # Single model with parallel episodes and games
   python scripts/runner.py --config src/game_reasoning_arena/configs/human_vs_random_config.yaml \
     --override use_ray=true parallel_episodes=true

   # Multiple models in parallel (maximum speed)
   python scripts/run_ray_multi_model.py \
     --config src/game_reasoning_arena/configs/ray_multi_model.yaml \
     --override use_ray=true

What's Next?
------------

* Learn about :doc:`games` supported by the platform
* Explore different :doc:`agents` types
* Check out :doc:`experiments` for Ray parallelization details
* Check out detailed :doc:`examples`
* Read the full :doc:`api_reference`