# Path: pytorchrl/sampler/parallel_sampler.py
# def _worker_init(G, id):
# def initialize(n_parallel):
# def _get_scoped_G(G, scope):
# def _worker_populate_task(G, env, policy, scope=None):
# def _worker_terminate_task(G, scope=None):
# def populate_task(env, policy, scope=None):
# def terminate_task(scope=None):
# def _worker_set_seed(_, seed):
# def set_seed(seed):
# def _worker_set_policy_params(G, params, scope=None):
# def _worker_set_env_params(G,params,scope=None):
# def _worker_collect_one_path(G, max_path_length, scope=None):
# def sample_paths(
#         policy_params,
#         max_samples,
#         max_path_length=np.inf,
#         env_params=None,
#         scope=None):
# def truncate_paths(paths, max_samples):
#     G = _get_scoped_G(G, scope)
#     G = _get_scoped_G(G, scope)
#         G = _get_scoped_G(singleton_pool.G, scope)
#     G = _get_scoped_G(G, scope)
#     G = _get_scoped_G(G, scope)
#     G = _get_scoped_G(G, scope)
# 
# Path: pytorchrl/sampler/utils.py
# def rollout(env, agent, max_path_length=np.inf, animated=False, speedup=1,
#             always_return_paths=False):
#     observations = []
#     actions = []
#     rewards = []
#     agent_infos = []
#     env_infos = []
#     o = env.reset()
#     agent.reset()
#     path_length = 0
#     if animated:
#         env.render()
#     while path_length < max_path_length:
#         a, agent_info = agent.get_action(o)
#         # print(a)
#         next_o, r, d, env_info = env.step(a)
#         observations.append(o)
#         rewards.append(r)
#         actions.append(a)
#         agent_infos.append(agent_info)
#         env_infos.append(env_info)
#         path_length += 1
#         if d:
#             break
#         o = next_o
#         if animated:
#             env.render()
#             timestep = 0.05
#             time.sleep(timestep / speedup)
#     if animated and not always_return_paths:
#         return
# 
#     return dict(
#         observations=tensor_utils.stack_tensor_list(observations),
#         actions=tensor_utils.stack_tensor_list(actions),
#         rewards=tensor_utils.stack_tensor_list(rewards),
#         agent_infos=tensor_utils.stack_tensor_dict_list(agent_infos),
#         env_infos=tensor_utils.stack_tensor_dict_list(env_infos),
#     )


import copy
import numpy as np
import pyprind
import rllab.misc.logger as logger
import torch
import torch.nn as nn
from rllab.algos.base import RLAlgorithm
from rllab.algos.ddpg import SimpleReplayPool
from rllab.misc import special
from rllab.misc import ext
from torch.autograd import Variable
from torch import optim
from pytorchrl.sampler import parallel_sampler
from pytorchrl.sampler.utils import rollout
            form.
        obs_val (numpy.ndarray): states draw from mini-batch, should have
            the same amount of rows as expected_qval.
        actions_val (numpy.ndarray): actions draw from mini-batch, should
            have the same amount of rows as expected_qval.
        """
        # Create Variable for input and output, we do not need gradient
        # of loss with respect to these variables
        obs = Variable(torch.from_numpy(obs_val)).type(
            torch.FloatTensor)
        actions = Variable(torch.from_numpy(actions_val)).type(
            torch.FloatTensor)
        expected_q = Variable(torch.from_numpy(expected_qval)).type(
            torch.FloatTensor)

        q_vals = self.qf(obs, actions)

        # Define loss function
        loss_fn = nn.MSELoss()
        loss = loss_fn(q_vals, expected_q)

        # Backpropagation and gradient descent
        self.qf_optimizer.zero_grad()
        loss.backward()
        self.qf_optimizer.step()

        return loss.data.numpy()

    def evaluate(self, epoch, pool):
        logger.log("Collecting samples for evaluation")

# Path: pytorchrl/sampler/parallel_sampler.py
# def _worker_init(G, id):
# def initialize(n_parallel):
# def _get_scoped_G(G, scope):
# def _worker_populate_task(G, env, policy, scope=None):
# def _worker_terminate_task(G, scope=None):
# def populate_task(env, policy, scope=None):
# def terminate_task(scope=None):
# def _worker_set_seed(_, seed):
# def set_seed(seed):
# def _worker_set_policy_params(G, params, scope=None):
# def _worker_set_env_params(G,params,scope=None):
# def _worker_collect_one_path(G, max_path_length, scope=None):
# def sample_paths(
#         policy_params,
#         max_samples,
#         max_path_length=np.inf,
#         env_params=None,
#         scope=None):
# def truncate_paths(paths, max_samples):
#     G = _get_scoped_G(G, scope)
#     G = _get_scoped_G(G, scope)
#         G = _get_scoped_G(singleton_pool.G, scope)
#     G = _get_scoped_G(G, scope)
#     G = _get_scoped_G(G, scope)
#     G = _get_scoped_G(G, scope)
# 
# Path: pytorchrl/sampler/utils.py
# def rollout(env, agent, max_path_length=np.inf, animated=False, speedup=1,
#             always_return_paths=False):
#     observations = []
#     actions = []
#     rewards = []
#     agent_infos = []
#     env_infos = []
#     o = env.reset()
#     agent.reset()
#     path_length = 0
#     if animated:
#         env.render()
#     while path_length < max_path_length:
#         a, agent_info = agent.get_action(o)
#         # print(a)
#         next_o, r, d, env_info = env.step(a)
#         observations.append(o)
#         rewards.append(r)
#         actions.append(a)
#         agent_infos.append(agent_info)
#         env_infos.append(env_info)
#         path_length += 1
#         if d:
#             break
#         o = next_o
#         if animated:
#             env.render()
#             timestep = 0.05
#             time.sleep(timestep / speedup)
#     if animated and not always_return_paths:
#         return
# 
#     return dict(
#         observations=tensor_utils.stack_tensor_list(observations),
#         actions=tensor_utils.stack_tensor_list(actions),
#         rewards=tensor_utils.stack_tensor_list(rewards),
#         agent_infos=tensor_utils.stack_tensor_dict_list(agent_infos),
#         env_infos=tensor_utils.stack_tensor_dict_list(env_infos),
#     )


# Path: pytorchrl/algos/naf.py
import copy
import numpy as np
import pyprind
import rllab.misc.logger as logger
import torch
import torch.nn as nn
from rllab.algos.base import RLAlgorithm
from rllab.algos.ddpg import SimpleReplayPool
from rllab.misc import special
from rllab.misc import ext
from torch.autograd import Variable
from torch import optim
from pytorchrl.sampler import parallel_sampler
from pytorchrl.sampler.utils import rollout

            form.
        obs_val (numpy.ndarray): states draw from mini-batch, should have
            the same amount of rows as expected_qval.
        actions_val (numpy.ndarray): actions draw from mini-batch, should
            have the same amount of rows as expected_qval.
        """
        # Create Variable for input and output, we do not need gradient
        # of loss with respect to these variables
        obs = Variable(torch.from_numpy(obs_val)).type(
            torch.FloatTensor)
        actions = Variable(torch.from_numpy(actions_val)).type(
            torch.FloatTensor)
        expected_q = Variable(torch.from_numpy(expected_qval)).type(
            torch.FloatTensor)

        q_vals = self.qf(obs, actions)

        # Define loss function
        loss_fn = nn.MSELoss()
        loss = loss_fn(q_vals, expected_q)

        # Backpropagation and gradient descent
        self.qf_optimizer.zero_grad()
        loss.backward()
        self.qf_optimizer.step()

        return loss.data.numpy()

    def evaluate(self, epoch, pool):
        logger.log("Collecting samples for evaluation")

        paths = parallel_sampler.sample_paths(