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import sys
from six import StringIO
from gym import utils
from gym.envs.toy_text import discrete
import numpy as np

MAP = [
    "+---------+",
    "|R: | : :G|",
    "| : : : : |",

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# activation command
# 'workon rl'

import gym
import numpy as np

env = gym.make('Taxi-v2')
NUM_OF_EPISODE = 10000

"""Note for reset

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import numpy as np
from scipy.spatial import procrustes
from scipy.linalg import orthogonal_procrustes
import matplotlib.pyplot as plt
from matplotlib.transforms import Affine2D
import argparse
import dlib
import cv2
from collections import OrderedDict

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# https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/installation.md
git clone https://github.com/CMU-Perceptual-Computing-Lab/openpose
cd openpose
sudo apt-get install cmake-qt-gui

sudo bash ./ubuntu/install_cmake.sh
sudo apt-get install libopencv-dev

sudo ubuntu/install_cuda.sh
sudo ubuntu/install_cudnn.sh

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[apt]
sudo apt-get -y install vim git
sudo apt-get update
# https://github.com/openai/mlsh/blob/58f527ab7e3397eeb723a7309852b6d8791d5c24/gym/Dockerfile
sudo apt-get install -y libav-tools
sudo apt-get install -y python-numpy
sudo apt-get install -y python-scipy
sudo apt-get install -y python-pyglet
sudo apt-get install -y python-setuptools
sudo apt-get install -y libpq-dev

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import tensorflow as tf
import numpy as np

bandits = [0.2, 0, -0.2, -5]
num_bandits = len(bandits)

def pull_bandit(bandit):
    result = np.random.randn(1)
    if result > bandit:
        return 1

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import gym
import numpy as np

env = gym.make('Taxi-v2')
env.reset()
print(env.observation_space.n)
print(env.action_space.n)
env.render()

Q = np.zeros([env.observation_space.n, env.action_space.n])

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Dataset for Reinforcement Learning

• DQN Replay Dataset by Google AI
  • context: An Optimistic Perspective on Offline Reinforcement Learning

labeled video datasets

Human activity

• ActivityNet

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- hosts: 127.0.0.1
  connection: local
  vars:
    pip_packages:
      - tensorflow
      - pandas
      - matplotlib
  tasks:
    - name: install ros-kinetic-viewer
      apt: