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# coding=utf-8
# Copyright 2019 The SEED Authors
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

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net = nn.Sequential(
        nn.Conv2d(1, 64, 3),
        nn.BatchNorm2d(64, momentum=1, affine=True),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(2, 2),
        nn.Conv2d(64, 64, 3),
        nn.BatchNorm2d(64, momentum=1, affine=True),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(2, 2),
        nn.Conv2d(64, 64, 3),

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index ede2865..de5eb9f 100755
--- a/examples/maml-omniglot.py
+++ b/examples/maml-omniglot.py
@@ -30,6 +30,7 @@ import higher

 from omniglot_loaders import OmniglotNShot

+
 def main():
     argparser = argparse.ArgumentParser()

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import dmc2gym
import numpy as np
import gym
import sys

seed = int(sys.argv[1])
env = dmc2gym.make(
        'point_mass',
        'easy',
        seed,

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import dmc2gym
import numpy as np
import gym
import sys

seed = int(sys.argv[1])
env = dmc2gym.make(
        'point_mass',
        'easy',
        seed,

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import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init
from torch.autograd import Variable

from models.utils import *


class LayerNormGRUCell(nn.GRUCell):
    def __init__(self, input_size, hidden_size, bias=True):

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#!/usr/bin/env python
"""
PyTorch implementation of DQN
Paper: https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf

"""

import argparse
import gym
from gym import wrappers

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import argparse
import pdb

import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import torch.nn.functional as F

import numpy as np

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#!/usr/local/bin/python
"""
Q-learning with value fucntion approximation
"""

import argparse
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

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#!/usr/local/bin/python
"""
Q-learning - off policy TD(0) learning.

Q(S, A) <- Q(S, A) + alpha * ((R + gamma * max(Q(S', A'))) - Q(S, A))
A ~ e-greedy from pi(A|S)
"""

import argparse
import numpy as np