check_run.py

import torch as tc
import numpy as np
import scipy
# import gym  # allowed but not included in env, yet...

from kaggle_environments import evaluate, make, utils

# Make and view the env
env = make("connectx", debug=True)
env.render()

def my_agent(observation, configuration):
    from random import choice
    return choice([c for c in range(configuration.columns) if observation.board[c] == 0])

env.reset()
# Play as the first agent against default "random" agent.
env.run([my_agent, "random"])
env.render(mode="ansi", width=500, height=450)

# Play as first position against random agent.
trainer = env.train([None, "random"])

observation = trainer.reset()

while not env.done:
    my_action = my_agent(observation, env.configuration)
    print("Action taken:", my_action)
    observation, reward, done, info = trainer.step(my_action)
    # env.render(mode="ansi", width=100, height=90, header=False, controls=False)
env.render()

# EVALUATE - read this section
def mean_reward(rewards):
    return sum(r[0] for r in rewards) / float(len(rewards))

# Run multiple episodes to estimate its performance.
print("My Agent vs Random Agent:", mean_reward(evaluate("connectx", [my_agent, "random"], num_episodes=10)))
# Takes longer:
print("My Agent vs Negamax Agent:", mean_reward(evaluate("connectx", [my_agent, "negamax"], num_episodes=3)))

# If in ipython, can do an interactive game with:
# env.play([None, "negamax"], width=500, height=450)