MegaYEye

#ifdef SHELL
g++ -Wall -Werror -g -I../../cclib/rapidjson/include $0 && ./a.out
exit 0
#endif

// Output is:
// {"project":"rapidjson","stars":11}
// {"Name":"XYZ","Rollnumer":2,"array":["hello","world"],"Marks":{"Math":"50","Science":"70","English":"50","Social Science":"70"}}
// {"FromEmail":"sender@gmail.com","FromName":"Sender's name","Subject":"My subject","Recipients":[{"Email":"recipient@gmail.com"}],"Text-part":"this is my text"}

MegaYEye

""" 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

MegaYEye

#!/usr/bin/env python2
import os
import gym
import sys
import random
import itertools
from time import time
from copy import copy
from math import sqrt, log

MegaYEye

# ----------------------------------------------------
# 1: NVIDIA Graphics Driver Installation
# ----------------------------------------------------

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-update
sudo apt install nvidia-384 nvidia-384-dev

# ----------------------------------------------------
# 2: Blacklist noveau-graphics-driver

MegaYEye

"""
solving pendulum using actor-critic model
"""

import gym
import numpy as np 
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Input
from keras.layers.merge import Add, Multiply
from keras.optimizers import Adam