andrewliao11

# modified from https://gist.github.com/sorenbouma/6502fbf55ecdf988aa247ef7f60a9546
import gym
import numpy as np
import matplotlib.pyplot as plt

env = gym.make('CartPole-v0')
env.render(close=True)
#vector of means(mu) and standard dev(sigma) for each paramater
mu=np.random.uniform(size=env.observation_space.shape)
sigma=np.random.uniform(low=0.001,size=env.observation_space.shape)

andrewliao11

# for model in `trpo_mpi`, `ppo`

class CnnPolicy():
  def __init__():
    # build graph
    _ = conv2d()
    _ = conv2d()
    
  def step():
    sess.run(act, feed_dict)

andrewliao11

# code locate in baselines/gail

def sample(algo, load_model_path, policy_fn):
  
  assert algo in ['trpo', 'ppo', 'acktr', 'ddpg', 'a2c']
  if algo in ['trpo', 'ppo']:
    with tf.Session() as sess:
      # manually build graph 
      policy = policy_fn()
      # load model

andrewliao11

import argparse
import gym
import numpy as np
from itertools import count
from collections import namedtuple, deque

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

andrewliao11

# using pytorch==0.4.0

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.rnn import RNNCellBase
from torch.nn._functions.thnn import rnnFusedPointwise as fusedBackend

andrewliao11

import torch
import imageio
import numpy as np
import seaborn
import matplotlib.pyplot as plt
import matplotlib
torch.manual_seed(1)


# data generation: y = ax + b

andrewliao11

import imageio
import numpy as np
from utils import *

mode = 'sgd'    # sgd, fisher, or dig_fisher


X_train, X_test, t_train, t_test = get_data()
W = get_model()

andrewliao11

{"0": "tench", "1": "goldfish", "2": "great white shark", "3": "tiger shark", "4": "hammerhead", "5": "electric ray", "6": "stingray,", "7": "cock,", "8": "hen,", "9": "ostrich", "10": "brambling", "11": "goldfinch", "12": "house finch", "13": "junco", "14": "indigo bunting", "15": "robin", "16": "bulbul,", "17": "jay,", "18": "magpie,", "19": "chickadee,", "20": "water ouzel", "21": "kite,", "22": "bald eagle", "23": "vulture,", "24": "great grey owl", "25": "European fire salamander", "26": "common newt", "27": "eft,", "28": "spotted salamander", "29": "axolotl", "30": "bullfrog", "31": "tree frog", "32": "tailed frog", "33": "loggerhead", "34": "leatherback turtle", "35": "mud turtle,", "36": "terrapin,", "37": "box turtle", "38": "banded gecko,", "39": "common iguana", "40": "American chameleon", "41": "whiptail", "42": "agama,", "43": "frilled lizard", "44": "alligator lizard,", "45": "Gila monster", "46": "green lizard", "47": "African chameleon", "48": "Komodo dragon", "49": "African crocodile", "50": 

andrewliao11

# inspired by the post: https://tanelp.github.io/posts/a-bug-that-plagues-thousands-of-open-source-ml-projects/
# tl;dr 
# If you are using numpy random generator with `torch.utils.data.Dataset`, 
# you might get identical results either across different workers or epochs

# disclaimer: this might not be the best choice since setting worker to be persistent requires additional RAM. 
# Welcome for any idea
# Here's a simple fix with torch>=1.7.0
# See the original example here: https://tanelp.github.io/posts/a-bug-that-plagues-thousands-of-open-source-ml-projects/#a-minimal-example