Super Auto Pets Gym

sapai-gym provides an OpenAI Gym environment for Super Auto Pets. This Gym environments provide a standard interface to train reinforcement learning (RL) models for Super Auto Pets and is compatible with any tools that accept Gym interfaces, including Stable Baselines3.

sapai-gym is built on top of sapai, which is a python implementation of Super Auto Pets.

Installation

Clone the repo and install dependencies

git clone https://github.com/alexdriedger/sapai-gym.git
cd sapai-gym
python setup.py install

Example Usage

SuperAutoPetsEnv implements the gym.Env interface. Here is a basic example that takes random actions

from sapai_gym import SuperAutoPetsEnv

def opponent_generator(num_turns):
    # Returns teams to fight against in the gym 
    return []

env = SuperAutoPetsEnv(opponent_generator, valid_actions_only=False)
obs = env.reset()

for step_num in range(1000):
    if step_num % 100 == 0:
        print(f"Step {step_num}")

    # Random actions
    action = env.action_space.sample()
    obs, reward, done, info = env.step(action)

    if done:
        obs = env.reset()
env.close()

Training RL Agent Using Stable Baselines3

from sb3_contrib import MaskablePPO
from sb3_contrib.common.maskable.evaluation import evaluate_policy
from sb3_contrib.common.maskable.utils import get_action_masks

def train_with_masks():
    env = SuperAutoPetsEnv(opponent_generator, valid_actions_only=True)

    model = MaskablePPO("MlpPolicy", env, verbose=1)
    model.learn(total_timesteps=10000)
    evaluate_policy(model, env, n_eval_episodes=20, reward_threshold=1, warn=False)
    obs = env.reset()
    
    num_games = 0
    while num_games < 100:
        # Predict outcome with model
        action_masks = get_action_masks(env)
        action, _states = model.predict(obs, action_masks=action_masks, deterministic=True)

        obs, reward, done, info = env.step(action)
        if done:
            num_games += 1
            obs = env.reset()
    env.close()

Action & Observation Space

The action space is a Discrete space of the total number of possible actions in Super Auto Pets (63 different actions in total without counting freezing and rearranging teams). For example, there are at most 6 pets available in the shop for purchase, so there are 6 buy_pet actions in the action space.

For observations, categorical features (pet names, pet statuses, and food names) are one-hot encoded. Attack and health are divided by 50, to remain in [0, 1]. All other features are scaled to [0, 1].

Opponent Generation

In Super Auto Pets, when you end your turn in the shop, you fight an opponent. The question of how to generate this opponent when simulating Super Auto Pets in a controlled environment is interesting and could have multiple different answers and implementations.