import jax.numpy as jnp

from pipeline import Pipeline
from algorithm.neat import *

from problem.rl_env import GymNaxEnv


def action_policy(randkey, forward_func, obs):
    return jnp.argmax(forward_func(obs))


if __name__ == "__main__":
    pipeline = Pipeline(
        algorithm=NEAT(
            species=DefaultSpecies(
                genome=DefaultGenome(
                    num_inputs=4,
                    num_outputs=2,
                    max_nodes=50,
                    max_conns=100,
                    # output_transform=lambda out: jnp.argmax(
                    #     out
                    # ),  # the action of cartpole is {0, 1}
                ),
                pop_size=10000,
                species_size=10,
            ),
        ),
        problem=GymNaxEnv(
            env_name="CartPole-v1", repeat_times=5, action_policy=action_policy
        ),
        generation_limit=10000,
        fitness_target=500,
    )

    # initialize state
    state = pipeline.setup()
    # print(state)
    # run until terminate
    state, best = pipeline.auto_run(state)