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env.py
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env.py
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"""
@author: Viet Nguyen <nhviet1009@gmail.com>
"""
import gym_super_mario_bros
from gym.spaces import Box
from gym import Wrapper
from nes_py.wrappers import JoypadSpace
from gym_super_mario_bros.actions import SIMPLE_MOVEMENT, COMPLEX_MOVEMENT, RIGHT_ONLY
import cv2
import numpy as np
import subprocess as sp
import torch.multiprocessing as mp
class Monitor:
def __init__(self, width, height, saved_path):
self.command = ["ffmpeg", "-y", "-f", "rawvideo", "-vcodec", "rawvideo", "-s", "{}X{}".format(width, height),
"-pix_fmt", "rgb24", "-r", "60", "-i", "-", "-an", "-vcodec", "mpeg4", saved_path]
try:
self.pipe = sp.Popen(self.command, stdin=sp.PIPE, stderr=sp.PIPE)
except FileNotFoundError:
pass
def record(self, image_array):
self.pipe.stdin.write(image_array.tostring())
def process_frame(frame):
if frame is not None:
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
frame = cv2.resize(frame, (84, 84))[None, :, :] / 255.
return frame
else:
return np.zeros((1, 84, 84))
class CustomReward(Wrapper):
def __init__(self, env=None, monitor=None):
super(CustomReward, self).__init__(env)
self.observation_space = Box(low=0, high=255, shape=(1, 84, 84))
self.curr_score = 0
if monitor:
self.monitor = monitor
else:
self.monitor = None
def step(self, action):
state, reward, done, info = self.env.step(action)
if self.monitor:
self.monitor.record(state)
state = process_frame(state)
reward += (info["score"] - self.curr_score) / 40.
self.curr_score = info["score"]
if done:
if info["flag_get"]:
reward += 50
else:
reward -= 50
return state, reward / 10., done, info
def reset(self):
self.curr_score = 0
return process_frame(self.env.reset())
class CustomSkipFrame(Wrapper):
def __init__(self, env, skip=4):
super(CustomSkipFrame, self).__init__(env)
self.observation_space = Box(low=0, high=255, shape=(skip, 84, 84))
self.skip = skip
self.states = np.zeros((skip, 84, 84), dtype=np.float32)
def step(self, action):
total_reward = 0
last_states = []
for i in range(self.skip):
state, reward, done, info = self.env.step(action)
total_reward += reward
if i >= self.skip / 2:
last_states.append(state)
if done:
self.reset()
return self.states[None, :, :, :].astype(np.float32), total_reward, done, info
max_state = np.max(np.concatenate(last_states, 0), 0)
self.states[:-1] = self.states[1:]
self.states[-1] = max_state
return self.states[None, :, :, :].astype(np.float32), total_reward, done, info
def reset(self):
state = self.env.reset()
self.states = np.concatenate([state for _ in range(self.skip)], 0)
return self.states[None, :, :, :].astype(np.float32)
def create_train_env(world, stage, actions, output_path=None):
env = gym_super_mario_bros.make("SuperMarioBros-{}-{}-v0".format(world, stage))
if output_path:
monitor = Monitor(256, 240, output_path)
else:
monitor = None
env = JoypadSpace(env, actions)
env = CustomReward(env, monitor)
env = CustomSkipFrame(env)
return env
class MultipleEnvironments:
def __init__(self, world, stage, action_type, num_envs, output_path=None):
self.agent_conns, self.env_conns = zip(*[mp.Pipe() for _ in range(num_envs)])
if action_type == "right":
actions = RIGHT_ONLY
elif action_type == "simple":
actions = SIMPLE_MOVEMENT
else:
actions = COMPLEX_MOVEMENT
self.envs = [create_train_env(world, stage, actions, output_path=output_path) for _ in range(num_envs)]
self.num_states = self.envs[0].observation_space.shape[0]
self.num_actions = len(actions)
for index in range(num_envs):
process = mp.Process(target=self.run, args=(index,))
process.start()
self.env_conns[index].close()
def run(self, index):
self.agent_conns[index].close()
while True:
request, action = self.env_conns[index].recv()
if request == "step":
self.env_conns[index].send(self.envs[index].step(action.item()))
elif request == "reset":
self.env_conns[index].send(self.envs[index].reset())
else:
raise NotImplementedError