<gh_stars>10-100
# Code is from OpenAI baseline.
#https://github.com/openai/baselines/tree/master/baselines/common/vec_env

import numpy as np
from multiprocessing import Process, Pipe

import numpy as np
class RunningMeanStd(object):
    # https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm
    def __init__(self, epsilon=1e-4, shape=()):
        self.mean = np.zeros(shape, 'float64')
        self.var = np.ones(shape, 'float64')
        self.count = epsilon

    def update(self, x):
        batch_mean = np.mean(x, axis=0)
        batch_var = np.var(x, axis=0)
        batch_count = x.shape[0]
        self.update_from_moments(batch_mean, batch_var, batch_count)

    def update_from_moments(self, batch_mean, batch_var, batch_count):
        delta = batch_mean - self.mean
        tot_count = self.count + batch_count

        new_mean = self.mean + delta * batch_count / tot_count
        m_a = self.var * (self.count)
        m_b = batch_var * (batch_count)
        M2 = m_a + m_b + np.square(delta) * self.count * batch_count / (self.count + batch_count)
        new_var = M2 / (self.count + batch_count)

        new_count = batch_count + self.count

        self.mean = new_mean
        self.var = new_var
        self.count = new_count


def worker(remote, parent_remote, env_fn_wrapper):
    parent_remote.close()
    env = env_fn_wrapper.x()
    while True:
        cmd, data = remote.recv()
        if cmd == 'step':
            ob, reward, done, info = env.step(data)
            if done:
                ob = env.reset()
            remote.send((ob, reward, done, info))
        elif cmd == 'reset':
            ob = env.reset()
            remote.send(ob)
        elif cmd == 'reset_task':
            ob = env.reset_task()
            remote.send(ob)
        elif cmd == 'close':
            remote.close()
            break
        elif cmd == 'get_spaces':
            remote.send((env.observation_space, env.action_space))
        else:
            raise NotImplementedError

class VecEnv(object):
    """
    An abstract asynchronous, vectorized environment.
    """
    def __init__(self, num_envs, observation_space, action_space):
        self.num_envs = num_envs
        self.observation_space = observation_space
        self.action_space = action_space

    def reset(self):
        """
        Reset all the environments and return an array of
        observations, or a tuple of observation arrays.
        If step_async is still doing work, that work will
        be cancelled and step_wait() should not be called
        until step_async() is invoked again.
        """
        pass

    def step_async(self, actions):
        """
        Tell all the environments to start taking a step
        with the given actions.
        Call step_wait() to get the results of the step.
        You should not call this if a step_async run is
        already pending.
        """
        pass

    def step_wait(self):
        """
        Wait for the step taken with step_async().
        Returns (obs, rews, dones, infos):
         - obs: an array of observations, or a tuple of
                arrays of observations.
         - rews: an array of rewards
         - dones: an array of "episode done" booleans
         - infos: a sequence of info objects
        """
        pass

    def close(self):
        """
        Clean up the environments' resources.
        """
        pass

    def step(self, actions):
        self.step_async(actions)
        return self.step_wait()

class VecEnvWrapper(VecEnv):
    def __init__(self, venv, observation_space=None, action_space=None):
        self.venv = venv
        VecEnv.__init__(self,
            num_envs=venv.num_envs,
            observation_space=observation_space or venv.observation_space,
            action_space=action_space or venv.action_space)

    def step_async(self, actions):
        self.venv.step_async(actions)

    def reset(self):
        pass

    def step_wait(self):
        pass

    def close(self):
        return self.venv.close()

    def render(self):
        self.venv.render()


class CloudpickleWrapper(object):
    """
    Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle)
    """
    def __init__(self, x):
        self.x = x
    def __getstate__(self):
        import cloudpickle
        return cloudpickle.dumps(self.x)
    def __setstate__(self, ob):
        import pickle
        self.x = pickle.loads(ob)

class SubprocVecEnv(VecEnv):
    def __init__(self, env_fns, spaces=None):
        """
        envs: list of gym environments to run in subprocesses
        """
        self.waiting = False
        self.closed = False
        nenvs = len(env_fns)
        self.nenvs = nenvs
        self.remotes, self.work_remotes = zip(*[Pipe() for _ in range(nenvs)])
        self.ps = [Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn)))
            for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)]
        for p in self.ps:
            p.daemon = True # if the main process crashes, we should not cause things to hang
            p.start()
        for remote in self.work_remotes:
            remote.close()

        self.remotes[0].send(('get_spaces', None))
        observation_space, action_space = self.remotes[0].recv()
        VecEnv.__init__(self, len(env_fns), observation_space, action_space)

    def step_async(self, actions):
        for remote, action in zip(self.remotes, actions):
            remote.send(('step', action))
        self.waiting = True

    def step_wait(self):
        results = [remote.recv() for remote in self.remotes]
        self.waiting = False
        obs, rews, dones, infos = zip(*results)
        return np.stack(obs), np.stack(rews), np.stack(dones), infos

    def reset(self):
        for remote in self.remotes:
            remote.send(('reset', None))
        return np.stack([remote.recv() for remote in self.remotes])

    def reset_task(self):
        for remote in self.remotes:
            remote.send(('reset_task', None))
        return np.stack([remote.recv() for remote in self.remotes])

    def close(self):
        if self.closed:
            return
        if self.waiting:
            for remote in self.remotes:
                remote.recv()
        for remote in self.remotes:
            remote.send(('close', None))
        for p in self.ps:
            p.join()
            self.closed = True

    def __len__(self):
        return self.nenvs


class VecNormalize(VecEnvWrapper):
    """
    Vectorized environment base class
    """
    def __init__(self, venv, ob=True, ret=True, clipob=10., cliprew=10., gamma=0.99, epsilon=1e-8):
        VecEnvWrapper.__init__(self, venv)
        self.ob_rms = RunningMeanStd(shape=self.observation_space.shape) if ob else None
        self.ret_rms = RunningMeanStd(shape=()) if ret else None
        self.clipob = clipob
        self.cliprew = cliprew
        self.ret = np.zeros(self.num_envs)
        self.gamma = gamma
        self.epsilon = epsilon

    def step_wait(self):
        """
        Apply sequence of actions to sequence of environments
        actions -> (observations, rewards, news)
        where 'news' is a boolean vector indicating whether each element is new.
        """
        obs, rews, news, infos = self.venv.step_wait()
        self.ret = self.ret * self.gamma + rews
        obs = self._obfilt(obs)
        if self.ret_rms:
            self.ret_rms.update(self.ret)
            rews = np.clip(rews / np.sqrt(self.ret_rms.var + self.epsilon), -self.cliprew, self.cliprew)
        return obs, rews, news, infos

    def _obfilt(self, obs):
        if self.ob_rms:
            self.ob_rms.update(obs)
            obs = np.clip((obs - self.ob_rms.mean) / np.sqrt(self.ob_rms.var + self.epsilon), -self.clipob, self.clipob)
            return obs
        else:
            return obs

    def reset(self):
        """
        Reset all environments
        """
        obs = self.venv.reset()
        return self._obfilt(obs)


from gym import spaces
from collections import OrderedDict
import numpy as np

class DummyVecEnv(VecEnv):
    def __init__(self, env_fns):
        self.envs = [fn() for fn in env_fns]
        env = self.envs[0]
        VecEnv.__init__(self, len(env_fns), env.observation_space, env.action_space)
        shapes, dtypes = {}, {}
        self.keys = []
        obs_space = env.observation_space

        subspaces = {None: obs_space}

        for key, box in subspaces.items():
            shapes[key] = box.shape
            dtypes[key] = np.float32
            self.keys.append(key)

        self.buf_obs = { k: np.zeros((self.num_envs,) + tuple(shapes[k]), dtype=dtypes[k]) for k in self.keys }
        self.buf_dones = np.zeros((self.num_envs,), dtype=np.bool)
        self.buf_rews  = np.zeros((self.num_envs,), dtype=np.float32)
        self.buf_infos = [{} for _ in range(self.num_envs)]
        self.actions = None

    def step_async(self, actions):
        self.actions = actions

    def step_wait(self):
        for e in range(self.num_envs):
            obs, self.buf_rews[e], self.buf_dones[e], self.buf_infos[e] = self.envs[e].step(self.actions[e])
            if self.buf_dones[e]:
                obs = self.envs[e].reset()
            self._save_obs(e, obs)
        return (self._obs_from_buf(), np.copy(self.buf_rews), np.copy(self.buf_dones),
                self.buf_infos.copy())

    def reset(self):
        for e in range(self.num_envs):
            obs = self.envs[e].reset()
            self._save_obs(e, obs)
        return self._obs_from_buf()

    def close(self):
        return

    def render(self, mode='human'):
        return [e.render(mode=mode) for e in self.envs]

    def _save_obs(self, e, obs):
        for k in self.keys:
            if k is None:
                self.buf_obs[k][e] = obs
            else:
                self.buf_obs[k][e] = obs[k]

    def _obs_from_buf(self):
        if self.keys==[None]:
            return self.buf_obs[None]
        else:
            return self.buf_obs



# Code is from OpenAI baseline.
#https://github.com/openai/baselines/tree/master/baselines/common/vec_env

import numpy as np
from multiprocessing import Process, Pipe

import numpy as np
class RunningMeanStd(object):
    # https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm
    def __init__(self, epsilon=1e-4, shape=()):
        self.mean = np.zeros(shape, 'float64')
        self.var = np.ones(shape, 'float64')
        self.count = epsilon

    def update(self, x):
        batch_mean = np.mean(x, axis=0)
        batch_var = np.var(x, axis=0)
        batch_count = x.shape[0]
        self.update_from_moments(batch_mean, batch_var, batch_count)

    def update_from_moments(self, batch_mean, batch_var, batch_count):
        delta = batch_mean - self.mean
        tot_count = self.count + batch_count

        new_mean = self.mean + delta * batch_count / tot_count
        m_a = self.var * (self.count)
        m_b = batch_var * (batch_count)
        M2 = m_a + m_b + np.square(delta) * self.count * batch_count / (self.count + batch_count)
        new_var = M2 / (self.count + batch_count)

        new_count = batch_count + self.count

        self.mean = new_mean
        self.var = new_var
        self.count = new_count


def worker(remote, parent_remote, env_fn_wrapper):
    parent_remote.close()
    env = env_fn_wrapper.x()
    while True:
        cmd, data = remote.recv()
        if cmd == 'step':
            ob, reward, done, info = env.step(data)
            if done:
                ob = env.reset()
            remote.send((ob, reward, done, info))
        elif cmd == 'reset':
            ob = env.reset()
            remote.send(ob)
        elif cmd == 'reset_task':
            ob = env.reset_task()
            remote.send(ob)
        elif cmd == 'close':
            remote.close()
            break
        elif cmd == 'get_spaces':
            remote.send((env.observation_space, env.action_space))
        else:
            raise NotImplementedError

class VecEnv(object):
    """
    An abstract asynchronous, vectorized environment.
    """
    def __init__(self, num_envs, observation_space, action_space):
        self.num_envs = num_envs
        self.observation_space = observation_space
        self.action_space = action_space

    def reset(self):
        """
        Reset all the environments and return an array of
        observations, or a tuple of observation arrays.
        If step_async is still doing work, that work will
        be cancelled and step_wait() should not be called
        until step_async() is invoked again.
        """
        pass

    def step_async(self, actions):
        """
        Tell all the environments to start taking a step
        with the given actions.
        Call step_wait() to get the results of the step.
        You should not call this if a step_async run is
        already pending.
        """
        pass

    def step_wait(self):
        """
        Wait for the step taken with step_async().
        Returns (obs, rews, dones, infos):
         - obs: an array of observations, or a tuple of
                arrays of observations.
         - rews: an array of rewards
         - dones: an array of "episode done" booleans
         - infos: a sequence of info objects
        """
        pass

    def close(self):
        """
        Clean up the environments' resources.
        """
        pass

    def step(self, actions):
        self.step_async(actions)
        return self.step_wait()

class VecEnvWrapper(VecEnv):
    def __init__(self, venv, observation_space=None, action_space=None):
        self.venv = venv
        VecEnv.__init__(self,
            num_envs=venv.num_envs,
            observation_space=observation_space or venv.observation_space,
            action_space=action_space or venv.action_space)

    def step_async(self, actions):
        self.venv.step_async(actions)

    def reset(self):
        pass

    def step_wait(self):
        pass

    def close(self):
        return self.venv.close()

    def render(self):
        self.venv.render()


class CloudpickleWrapper(object):
    """
    Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle)
    """
    def __init__(self, x):
        self.x = x
    def __getstate__(self):
        import cloudpickle
        return cloudpickle.dumps(self.x)
    def __setstate__(self, ob):
        import pickle
        self.x = pickle.loads(ob)

class SubprocVecEnv(VecEnv):
    def __init__(self, env_fns, spaces=None):
        """
        envs: list of gym environments to run in subprocesses
        """
        self.waiting = False
        self.closed = False
        nenvs = len(env_fns)
        self.nenvs = nenvs
        self.remotes, self.work_remotes = zip(*[Pipe() for _ in range(nenvs)])
        self.ps = [Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn)))
            for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)]
        for p in self.ps:
            p.daemon = True # if the main process crashes, we should not cause things to hang
            p.start()
        for remote in self.work_remotes:
            remote.close()

        self.remotes[0].send(('get_spaces', None))
        observation_space, action_space = self.remotes[0].recv()
        VecEnv.__init__(self, len(env_fns), observation_space, action_space)

    def step_async(self, actions):
        for remote, action in zip(self.remotes, actions):
            remote.send(('step', action))
        self.waiting = True

    def step_wait(self):
        results = [remote.recv() for remote in self.remotes]
        self.waiting = False
        obs, rews, dones, infos = zip(*results)
        return np.stack(obs), np.stack(rews), np.stack(dones), infos

    def reset(self):
        for remote in self.remotes:
            remote.send(('reset', None))
        return np.stack([remote.recv() for remote in self.remotes])

    def reset_task(self):
        for remote in self.remotes:
            remote.send(('reset_task', None))
        return np.stack([remote.recv() for remote in self.remotes])

    def close(self):
        if self.closed:
            return
        if self.waiting:
            for remote in self.remotes:
                remote.recv()
        for remote in self.remotes:
            remote.send(('close', None))
        for p in self.ps:
            p.join()
            self.closed = True

    def __len__(self):
        return self.nenvs


class VecNormalize(VecEnvWrapper):
    """
    Vectorized environment base class
    """
    def __init__(self, venv, ob=True, ret=True, clipob=10., cliprew=10., gamma=0.99, epsilon=1e-8):
        VecEnvWrapper.__init__(self, venv)
        self.ob_rms = RunningMeanStd(shape=self.observation_space.shape) if ob else None
        self.ret_rms = RunningMeanStd(shape=()) if ret else None
        self.clipob = clipob
        self.cliprew = cliprew
        self.ret = np.zeros(self.num_envs)
        self.gamma = gamma
        self.epsilon = epsilon

    def step_wait(self):
        """
        Apply sequence of actions to sequence of environments
        actions -> (observations, rewards, news)
        where 'news' is a boolean vector indicating whether each element is new.
        """
        obs, rews, news, infos = self.venv.step_wait()
        self.ret = self.ret * self.gamma + rews
        obs = self._obfilt(obs)
        if self.ret_rms:
            self.ret_rms.update(self.ret)
            rews = np.clip(rews / np.sqrt(self.ret_rms.var + self.epsilon), -self.cliprew, self.cliprew)
        return obs, rews, news, infos

    def _obfilt(self, obs):
        if self.ob_rms:
            self.ob_rms.update(obs)
            obs = np.clip((obs - self.ob_rms.mean) / np.sqrt(self.ob_rms.var + self.epsilon), -self.clipob, self.clipob)
            return obs
        else:
            return obs

    def reset(self):
        """
        Reset all environments
        """
        obs = self.venv.reset()
        return self._obfilt(obs)


from gym import spaces
from collections import OrderedDict
import numpy as np

class DummyVecEnv(VecEnv):
    def __init__(self, env_fns):
        self.envs = [fn() for fn in env_fns]
        env = self.envs[0]
        VecEnv.__init__(self, len(env_fns), env.observation_space, env.action_space)
        shapes, dtypes = {}, {}
        self.keys = []
        obs_space = env.observation_space

        subspaces = {None: obs_space}

        for key, box in subspaces.items():
            shapes[key] = box.shape
            dtypes[key] = np.float32
            self.keys.append(key)

        self.buf_obs = { k: np.zeros((self.num_envs,) + tuple(shapes[k]), dtype=dtypes[k]) for k in self.keys }
        self.buf_dones = np.zeros((self.num_envs,), dtype=np.bool)
        self.buf_rews  = np.zeros((self.num_envs,), dtype=np.float32)
        self.buf_infos = [{} for _ in range(self.num_envs)]
        self.actions = None

    def step_async(self, actions):
        self.actions = actions

    def step_wait(self):
        for e in range(self.num_envs):
            obs, self.buf_rews[e], self.buf_dones[e], self.buf_infos[e] = self.envs[e].step(self.actions[e])
            if self.buf_dones[e]:
                obs = self.envs[e].reset()
            self._save_obs(e, obs)
        return (self._obs_from_buf(), np.copy(self.buf_rews), np.copy(self.buf_dones),
                self.buf_infos.copy())

    def reset(self):
        for e in range(self.num_envs):
            obs = self.envs[e].reset()
            self._save_obs(e, obs)
        return self._obs_from_buf()

    def close(self):
        return

    def render(self, mode='human'):
        return [e.render(mode=mode) for e in self.envs]

    def _save_obs(self, e, obs):
        for k in self.keys:
            if k is None:
                self.buf_obs[k][e] = obs
            else:
                self.buf_obs[k][e] = obs[k]

    def _obs_from_buf(self):
        if self.keys==[None]:
            return self.buf_obs[None]
        else:
            return self.buf_obs
# Code is from OpenAI baseline. #https://github.com/openai/baselines/tree/master/baselines/common/vec_env # https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm An abstract asynchronous, vectorized environment. Reset all the environments and return an array of         observations, or a tuple of observation arrays.         If step_async is still doing work, that work will         be cancelled and step_wait() should not be called         until step_async() is invoked again. Tell all the environments to start taking a step         with the given actions.         Call step_wait() to get the results of the step.         You should not call this if a step_async run is         already pending. Wait for the step taken with step_async().         Returns (obs, rews, dones, infos):          - obs: an array of observations, or a tuple of                 arrays of observations.          - rews: an array of rewards          - dones: an array of "episode done" booleans          - infos: a sequence of info objects Clean up the environments' resources. Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle) envs: list of gym environments to run in subprocesses # if the main process crashes, we should not cause things to hang Vectorized environment base class Apply sequence of actions to sequence of environments         actions -> (observations, rewards, news)         where 'news' is a boolean vector indicating whether each element is new. Reset all environments