def __init__(self, output_path, frame_shape, frames_per_sec):
self.proc = None
self.output_path = output_path
# Frame shape should be lines-first, so w and h are swapped
h, w, pixfmt = frame_shape
if pixfmt != 3 and pixfmt != 4:
raise error.InvalidFrame("Your frame has shape {}, but we require (w,h,3) or (w,h,4), i.e. RGB values for a w-by-h image, with an optional alpha channl.".format(frame_shape))
self.wh = (w,h)
self.includes_alpha = (pixfmt == 4)
self.frame_shape = frame_shape
self.frames_per_sec = frames_per_sec
if distutils.spawn.find_executable('avconv') is not None:
self.backend = 'avconv'
elif distutils.spawn.find_executable('ffmpeg') is not None:
self.backend = 'ffmpeg'
else:
raise error.DependencyNotInstalled("""Found neither the ffmpeg nor avconv executables. On OS X, you can install ffmpeg via `brew install ffmpeg`. On most Ubuntu variants, `sudo apt-get install ffmpeg` should do it. On Ubuntu 14.04, however, you'll need to install avconv with `sudo apt-get install libav-tools`.""")
self.start()
import numpy as np
import os
from rand_param_envs import gym
from rand_param_envs.gym import error, spaces
from rand_param_envs.gym import utils
from rand_param_envs.gym.utils import seeding
try:
import atari_py
except ImportError as e:
raise error.DependencyNotInstalled("{}. (HINT: you can install Atari dependencies by running 'pip install gym[atari]'.)".format(e))
import logging
logger = logging.getLogger(__name__)
def to_ram(ale):
ram_size = ale.getRAMSize()
ram = np.zeros((ram_size),dtype=np.uint8)
ale.getRAM(ram)
return ram
class AtariEnv(gym.Env, utils.EzPickle):
metadata = {'render.modes': ['human', 'rgb_array']}
def __init__(self, game='pong', obs_type='ram', frameskip=(2, 5), repeat_action_probability=0.):
"""Frameskip should be either a tuple (indicating a random range to
choose from, with the top value exclude), or an int."""
utils.EzPickle.__init__(self, game, obs_type)
assert obs_type in ('ram', 'image')
from rand_param_envs.gym import error
try:
import pachi_py
except ImportError as e:
# The dependency group [pachi] should match the name is setup.py.
raise error.DependencyNotInstalled(
'{}. (HINT: you may need to install the Go dependencies via "pip install gym[pachi]".)'.format(e))
import numpy as np
from rand_param_envs import gym
from rand_param_envs.gym import spaces
from rand_param_envs.gym.utils import seeding
from six import StringIO
import sys
import six
# The coordinate representation of Pachi (and pachi_py) is defined on a board
# with extra rows and columns on the margin of the board, so positions on the board
# are not numbers in [0, board_size**2) as one would expect. For this Go env, we instead
# use an action representation that does fall in this more natural range.
def _pass_action(board_size):
return board_size ** 2
def _resign_action(board_size):
return board_size ** 2 + 1
import os
from rand_param_envs.gym import error, spaces
from rand_param_envs.gym.utils import seeding
import numpy as np
from os import path
from rand_param_envs import gym
import six
try:
from rand_param_envs import mujoco_py
from rand_param_envs.mujoco_py.mjlib import mjlib
except ImportError as e:
raise error.DependencyNotInstalled(
"{}. (HINT: you need to install mujoco_py, and also perform the setup instructions here: https://github.com/openai/mujoco-py/.)"
.format(e))
class MujocoEnv(gym.Env):
"""Superclass for all MuJoCo environments.
"""
def __init__(self, model_path, frame_skip):
if model_path.startswith("/"):
fullpath = model_path
else:
fullpath = os.path.join(os.path.dirname(__file__), "assets",
model_path)
if not path.exists(fullpath):
raise IOError("File %s does not exist" % fullpath)
self.frame_skip = frame_skip
self.model = mujoco_py.MjModel(fullpath)