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1_sb_ppo_agent.py
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#import retro
import gym_super_mario_bros
import logging
import gym
import gym_super_mario_bros
import numpy as np
import sys
from nes_py.wrappers import BinarySpaceToDiscreteSpaceEnv
import random
from stable_baselines.ppo2.ppo2 import PPO2
from stable_baselines import PPO2
from stable_baselines.common.policies import CnnPolicy
from stable_baselines.common.vec_env import DummyVecEnv
movements = [
['NOP'],
['A'],
['B'],
['right'],
['right', 'A'],
['right', 'B'],
['right', 'A', 'B'],
['left'],
['left', 'A'],
['left', 'B'],
['left', 'A', 'B'],
# ['down'],
# ['up']
]
_env = gym_super_mario_bros.make('SuperMarioBros-v0')
#_env = gym_super_mario_bros.SuperMarioBrosEnv(frames_per_step=1, rom_mode='rectangle')
env = BinarySpaceToDiscreteSpaceEnv(_env, movements)
env = DummyVecEnv([lambda: env])
model = PPO2(policy=CnnPolicy, env=env, verbose=1)
model.learn(total_timesteps=10000)
obs = env.reset()
while True:
action, _info = model.predict(obs)
obs, rewards, dones, info = env.step(action)
print("학습끝")
print(rewards)
env.render()