jterrace

XVFB=/usr/bin/Xvfb
XVFBARGS=":1 -screen 0 1024x768x24 -ac +extension GLX +render -noreset"
PIDFILE=/var/run/xvfb.pid
case "$1" in
  start)
    echo -n "Starting virtual X frame buffer: Xvfb"
    start-stop-daemon --start --quiet --pidfile $PIDFILE --make-pidfile --background --exec $XVFB -- $XVFBARGS
    echo "."
    ;;
  stop)

ParkinT

When I first uncovered Collatz conjecture it made perfect sense to me. You are simply dividing an even number in half until you reach one and, if the number is not even you coerce it to be so.

This created a flash of inspiration for me to devise this simple routine.

{This could be used as the basis of a program to prove the "unsolved problem" of whether Collatz conjecture applies to ALL numbers greater than one.}

dergachev

# taken from http://www.piware.de/2011/01/creating-an-https-server-in-python/
# generate server.xml with the following command:
#    openssl req -new -x509 -keyout server.pem -out server.pem -days 365 -nodes
# run as follows:
#    python simple-https-server.py
# then in your browser, visit:
#    https://localhost:4443

import BaseHTTPServer, SimpleHTTPServer
import ssl

julionc

How to install PhantomJS on Ubuntu

Version: 1.9.8

Platform: x86_64

First, install or update to the latest system software.

sudo apt-get update
sudo apt-get install build-essential chrpath libssl-dev libxft-dev

joschu

Code used to obtain these results can be found at the url https://github.com/joschu/modular_rl, commit 50cdfdf375e69d86e3db6eb2ad0218ea6aebf371. The command line expression used for all the environments can be found in the text file below. Note that the same exact parameters and policies were used for all tasks, except for timesteps_per_batch, which was varied based on the difficulty of the task. The important parameters are:

• gamma=0.995: discount
• lam=0.97: see GAE paper for explanation
• agent=TrpoAgent: name of the class, which specifies policy and value function architecture. In this case, we used two hidden layers of size 64, with tanh activations
• cg_damping: multiple of the identity added for conjugate gradient

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

sorenbouma

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=state.shape)
sigma=np.random.uniform(low=0.001,size=state.shape)

ferrine

import math
from functools import wraps

from theano import tensor as T
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams

from lasagne import init
from lasagne.random import get_rng

__all__ = ['Accumulator', 'NormalApproximation', 'NormalApproximationScMix', 'bbpwrap']

justheuristic

alexlee-gk

import numpy as np
import theano
import theano.tensor as T
import lasagne.layers as L


class LocallyConnected2DLayer(L.Conv2DLayer):
    """Similar to Conv2DLayer except that the filter weights are unshared

    This implementation computes the output tensor by iterating over the filter