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

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

viktorklang

trait Enum { //DIY enum type
  import java.util.concurrent.atomic.AtomicReference //Concurrency paranoia

  type EnumVal <: Value //This is a type that needs to be found in the implementing class

  private val _values = new AtomicReference(Vector[EnumVal]()) //Stores our enum values

  //Adds an EnumVal to our storage, uses CCAS to make sure it's thread safe, returns the ordinal
  private final def addEnumVal(newVal: EnumVal): Int = { import _values.{get, compareAndSet => CAS}
    val oldVec = get