karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

gboeing

To distribute a package on pypi

Directory structure

/project/
    /package/
        __init__.py
        module.py
 setup.py

mikedh

import pyglet
import pyglet.gl as gl

import numpy as np

import os
import tempfile
import subprocess
import collections

andreh7

iandanforth

"""
Classic cart-pole system.

Pymunk version by Ian Danforth
"""

import math
import gym
import pygame
import pymunk

diegopacheco

sudo apt-get install autoconf automake libtool curl make g++ unzip -y
git clone https://github.com/google/protobuf.git
cd protobuf
git submodule update --init --recursive
./autogen.sh
make
make check
sudo make install
sudo ldconfig

tshanmukh

# importing copy as we have to copy the same image to different instances to visualize
import copy
plt.rcParams['figure.figsize'] = [14.0, 7.0]

# Set the parameters of the ORB algorithm by specifying the maximum number of keypoints to locate and
# the pyramid decimation ratio
orb = cv2.ORB_create(200, 2.0) # refer the opencv page for available arguments and usages

# computing the keypoints using the detectandcompute method, this accepts a grey scale image and None parameter
# is to let the ORB know we are not using any mask