ImarishaRL/african-boardgames-envs

**Status:** In development Imarisha Learning Suite ======================== **Imarisha is a reinforcement learning library for african board games.** The ``Imarisha learning`` suite provides a set of diverse, two-player board game environments that vary widely in complexity. From Ajua to Fanarona, the learning suite offers environments of increasing complexity for reinforcement learning research. Researchers can use the environments provided as stepping stones when testing the scalability and efficiency of their algorithms on increasingly harder problems. Basics ====== The Imarisha learning suite has a gym like interface for interaction. The following are the ``Env`` methods you should know: - `reset(self)`: Reset the environment's state. Returns `observation`. - `step(self, action)`: Step the environment by one timestep. Returns `observation`, `reward`, `done`, `info`. - `render(self)`: Render one frame of the environment. Supported systems ----------------- We currently support Linux and OS X running Python 3.5 -- 3.8 Installation ============ You can perform a minimal install of ``imarisha`` with: .. code:: shell git clone github.com cd african-boardgames-envs Environments ============ **Ajua** See `Ajua_rules.txt` for detailed description of Ajua environment. **Coming Soon:** - `Gulugufe` - `Tsoro Yematatu` - `Doki` - `Senet` - `Fanorona` Example ======== **This example runs a random policy in the ajua environment** .. code:: python import numpy as np import Ajua_env env = Ajua_env.AJUA() done = False steps = 0 agent_1 = 0 agent_2 = 0 while not done: steps += 1 action = np.random.randint(0, 18) state, reward, done, _ = env.step(action) if env.current_player == 9: agent_2 += reward else: agent_1 += reward print("\nAction: ",action, "Player: ", env.current_player, "Reward: ", reward) env.render() print(agent_2, agent_1) Resources ========= - `mail-imarisharl@gmail.com`