Integrating gym-super-mario-bros with FastAPI, SQLAlchemy, and Pydantic

The gym-super-mario-bros library provides an environment for training reinforcement learning agents in Super Mario Bros. games on the Nintendo Entertainment System (NES). In this article, we will explore how to integrate gym-super-mario-bros with other popular Python libraries such as FastAPI, SQLAlchemy, and Pydantic.

Installation

First, make sure you have gym-super-mario-bros installed by running the following command:

#shell
pip install gym-super-mario-bros

Usage with FastAPI

To use gym-super-mario-bros in FastAPI, we need to import the necessary modules and set up the gym environment. Here’s an example of how to do it:

#python
from fastapi import FastAPI
from nes_py.wrappers import JoypadSpace
import gym_super_mario_bros
from gym_super_mario_bros.actions import SIMPLE_MOVEMENT

app = FastAPI()

@app.get("/play_mario")
def play_mario():
    env = gym_super_mario_bros.make('SuperMarioBros-v0')
    env = JoypadSpace(env, SIMPLE_MOVEMENT)

    done = True
    for step in range(5000):
        if done:
            state = env.reset()
        state, reward, done, info = env.step(env.action_space.sample())

    env.close()

    return {"message": "Mario has finished playing"}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)

In this example, we define a FastAPI application and create a route /play_mario that simulates Mario playing the Super Mario Bros. game. We use the gym_super_mario_bros.make function to create the gym environment and the JoypadSpace wrapper to simplify the action space. Finally, we run the FastAPI application using uvicorn.

Integration with SQLAlchemy and Pydantic

To integrate gym-super-mario-bros with SQLAlchemy and Pydantic, we can create a database model and a Pydantic model that represents the game state. Here’s an example:

#python
from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
from pydantic import BaseModel

# SQLAlchemy models
engine = create_engine('sqlite:///mario.db')
Session = sessionmaker(bind=engine)
Base = declarative_base()

class GameResult(Base):
    __tablename__ = 'game_results'
    
    id = Column(Integer, primary_key=True)
    score = Column(Integer)
    time = Column(Integer)
    world = Column(Integer)
    level = Column(Integer)

# Pydantic models
class GameResultCreate(BaseModel):
    score: int
    time: int
    world: int
    level: int

class GameResultGet(BaseModel):
    id: int
    score: int
    time: int
    world: int
    level: int

In this example, we define a GameResult model using SQLAlchemy, which represents a game result with fields such as score, time, world, and level. We also define a GameResultCreate Pydantic model for creating new game results, and a GameResultGet Pydantic model for retrieving game results from the database.

To interact with the database, we can define CRUD operations using SQLAlchemy in our FastAPI routes:

#python
@app.post("/game_results")
def create_game_result(game_result: GameResultCreate):
    session = Session()
    db_game_result = GameResult(**game_result.dict())
    session.add(db_game_result)
    session.commit()
    session.refresh(db_game_result)
    session.close()
    return {"message": "Game result created"}

@app.get("/game_results/{game_result_id}", response_model=GameResultGet)
def read_game_result(game_result_id: int):
    session = Session()
    db_game_result = session.query(GameResult).get(game_result_id)
    session.close()
    return db_game_result

In this example, we create a new game result by receiving a GameResultCreate object and saving it to the database using SQLAlchemy. We also retrieve a game result by its ID and return it as a GameResultGet object.

Conclusion

In this article, we have explored how to integrate the gym-super-mario-bros library with FastAPI, SQLAlchemy, and Pydantic. By combining these powerful tools, we can create a complete pipeline for training reinforcement learning agents in the classic Super Mario Bros. game.

The integration of gym-super-mario-bros with FastAPI allows us to easily expose the game environment as a web service, allowing for remote training and experimentation. SQLAlchemy and Pydantic provide a convenient way to store and retrieve game results from a database, enabling us to analyze and monitor the performance of our agents.

Overall, the integration of these technologies enhances the capabilities of gym-super-mario-bros and opens up new possibilities for developing intelligent game-playing agents.