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mario_env.py
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mario_env.py
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import dm_env
import gym_super_mario_bros
import matplotlib
import numpy as np
from dm_env import specs
from dm_env import TimeStep
from gym_super_mario_bros.actions import SIMPLE_MOVEMENT
from gym_super_mario_bros.actions import RIGHT_ONLY
from gym_super_mario_bros.actions import COMPLEX_MOVEMENT
from nes_py.wrappers import JoypadSpace
matplotlib.use('TkAgg')
from matplotlib import pyplot as plt
from skimage.transform import rescale
from typing import Optional, Tuple, Callable, List
RGB2GRAY_COEFFICIENTS = np.array([0.3, 0.3, 0.4], dtype=np.float32)
MOVEMENTS_TYPES = {
"right_only": RIGHT_ONLY,
"simple": SIMPLE_MOVEMENT,
"simple_with_down": SIMPLE_MOVEMENT + ["down"],
"complex": COMPLEX_MOVEMENT,
}
class MarioEnvironment(dm_env.Environment):
def __init__(self,
skip_frames: int = 3,
img_rescale_pc: float = 0.4,
stack_func: Optional[
Callable[[List[np.ndarray]], np.ndarray]] = np.hstack,
stack_mode: str = "all",
grayscale: bool = True,
black_background: bool = True,
in_game_score_weight: float = 0.01,
movement_type: str = "simple",
world_and_level: Optional[Tuple[int, int]] = None,
idle_frames_threshold: Optional[int] = 1250,
colorful_rendering: bool = True,
) -> None:
assert stack_mode in ("first_and_last", "all")
self._stack_mode = stack_mode
env_name = (f"SuperMarioBros" if world_and_level is None
else "SuperMarioBros-%d-%d" % world_and_level)
env_name += f"-v{int(black_background)}"
self._smb_env = gym_super_mario_bros.make(env_name)
self._smb_env = JoypadSpace(self._smb_env,
MOVEMENTS_TYPES[movement_type])
self._actions_queue = []
self._colorful_env = None
if (grayscale or black_background) and colorful_rendering:
self._colorful_env = gym_super_mario_bros.make(
"SuperMarioBros-%d-%d-v0" % world_and_level
)
self._colorful_env = JoypadSpace(self._colorful_env,
MOVEMENTS_TYPES[movement_type])
self._stack_func = stack_func
self._grayscale = grayscale
self._score_weight = in_game_score_weight
self._idle_frames_threshold = idle_frames_threshold
self._last_score = 0
self._last_x = 40
self._idle_counter = 0
self._rescale_pc = img_rescale_pc
self._skip_frames = skip_frames
self._obs_shape = self.reset().observation.shape
self._num_actions = self._smb_env.action_space.n
def reset(self):
""" Returns the first `TimeStep` of a new episode. """
self._smb_env.reset()
self._last_score = 0
self._last_x = 40
self._idle_counter = 0
self._actions_queue = []
if self._colorful_env is not None:
self._colorful_env.reset()
return dm_env.restart(self.step(0).observation)
def _is_idle(self, info):
if self._idle_frames_threshold is None:
return False
x = info["x_pos"]
delta_x = x - self._last_x
self._last_x = x
if abs(delta_x) < 1:
self._idle_counter += 1
return self._idle_counter > self._idle_frames_threshold
self._idle_counter = 0
return False
def step(self, action) -> TimeStep:
""" Updates the environment's state. """
# NOTE:
# The gym_super_mario_bros environment reuses the numpy array it
# returns as observation. When stacking observations, this might be
# a source of bugs (all observations in the stack might be representing
# the same, final frame!), so always copy the arrays when doing that.
# The observation arrays are already being copied inside
# `self._preprocess_img`, so no explicit copying is needed here.
action = int(action)
initial_img, total_reward, done, info = self._smb_env.step(action)
self._actions_queue.append(action)
done = done or self._is_idle(info)
# Skipping frames:
if self._skip_frames > 0:
imgs = [self._process_img(initial_img)]
skip_count = 0
while skip_count < self._skip_frames:
skip_count += 1
if not done:
last_img, reward, done, info = self._smb_env.step(action)
self._actions_queue.append(action)
done = done or self._is_idle(info)
total_reward += reward
else:
last_img = np.zeros_like(initial_img)
if self._stack_mode == "all" or skip_count == self._skip_frames:
imgs.append(self._process_img(last_img))
obs = self._stack_func(imgs)
# Single frame:
else:
obs = self._process_img(initial_img)
score_diff = info["score"] - self._last_score
self._last_score = info["score"]
total_reward = np.float64(total_reward
+ self._score_weight * score_diff)
if done:
return dm_env.termination(reward=total_reward, observation=obs)
return dm_env.transition(reward=total_reward, observation=obs)
def observation_spec(self):
return dm_env.specs.BoundedArray(shape=self._obs_shape,
dtype=np.float32, name="image",
minimum=0, maximum=1)
def action_spec(self):
return dm_env.specs.DiscreteArray(dtype=np.int32, name="action",
num_values=self._num_actions)
def _process_img(self, img):
img = np.divide(img, 255)
img = img[50:, :, :]
if abs(self._rescale_pc - 1) > 1e-2:
img = rescale(img, scale=self._rescale_pc, multichannel=True)
if self._grayscale:
img = img @ RGB2GRAY_COEFFICIENTS
return img.astype(np.float32, copy=True)
def render(self, mode="human", return_all_imgs=False):
if return_all_imgs:
assert self._colorful_env is not None and mode == "rgb_array", (
"The option 'return_all_imgs' is valid only when using "
"colorful rendering and rgb array mode!"
)
# Regular rendering:
if self._colorful_env is None:
return self._smb_env.render(mode)
# Colorful rendering:
img_list = []
for action in self._actions_queue:
self._colorful_env.step(action)
if return_all_imgs:
# NOTE: make sure a copy of the returned rgb array is made!
img_list.append(self._colorful_env.render(mode).copy())
self._actions_queue = []
return img_list if return_all_imgs else self._colorful_env.render(mode)
def plot_obs(self, obs):
plt.imshow(obs, cmap="gray" if self._grayscale else None)
plt.show()
def close(self):
self._smb_env.close()