syuntoku14

import gym
from gym import wrappers
import numpy as np
import matplotlib.pyplot as plt

"""
 For practicing reinforcement learning

 observation:
    (position,velocity)

syuntoku14

syuntoku14

#!/usr/bin/env bash

function _func_create_md(){
	kyo=`date +%y%m%d`
	kyo_md="${kyo}.md"
	if ! [ -e "${kyo_md}" ]; then
		: > "${kyo_md}"
	fi
}

syuntoku14

#!/usr/bin/env bash

mass=`grep ^質量 "$1" | sed -e "s/[^0-9.]//g"`

echo "<mass value=\"$mass\"/>"

# extract ixx, ixy, ixz
temp=`grep Ixx "$1" | sed -e "s/[a-z = A-Z]//g"`

declare xx=() # declare matrix

syuntoku14

% Simulation of Kalman_Filter
% In this script, error_ellipse function is used. Please download here: 
% https://www.mathworks.com/matlabcentral/fileexchange/4705-error-ellipse

clear; close all;
fig = figure;
hold on;

% Initial State
x0 = [0, 0].';

syuntoku14

% Simulation of Kalman_Filter
% In this script, error_ellipse function is used. Please download here: 
% https://www.mathworks.com/matlabcentral/fileexchange/4705-error-ellipse

clear; close all;
fig = figure;
hold on;

% Initial State
x0 = [0, 0].';

syuntoku14

docker run -it --rm \
--net=host \
-e DISPLAY=$DISPLAY \
-v /home/syuntoku/.Xauthority:/headless/.Xauthority:rw \
-v /tmp/.X11-unix:/tmp/.X11-unix \
--entrypoint=/bin/bash \
lucasfischerberkeley/flowdesktop

# inside the container, run "conda activate flow-rllab"

syuntoku14

# install python packages
conda config --add channels conda-forge
conda install -y jupyter pytorch torchvision -c pytorch
conda install -y pyvirtualdisplay
yes | pip3 install torchvision
yes | pip3 install gym "gym[atari]"

# Configure jupyter notebook server
jupyter notebook --generate-config
echo "c = get_config()" >> ~/.jupyter/jupyter_notebook_config.py

syuntoku14

import argparse
import torch
import torch.nn as nn
import numpy as np
from torch.utils.data import Dataset, DataLoader
import sys
from torch.nn.utils import clip_grad_norm_
import parser
import torch
import os

syuntoku14

import argparse
import torch
import torch.nn as nn
import numpy as np
from torch.utils.data import Dataset, DataLoader
from pytorch_pretrained_bert import BertModel, BertTokenizer
import torch.nn.utils.rnn as rnn
import torch.nn.functional as F
import torch.utils.data as data
import sys