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def get_jacobian(net, x, noutputs): | |
x = x.squeeze() | |
n = x.size()[0] | |
x = x.repeat(noutputs, 1) | |
x.requires_grad_(True) | |
y = net(x) | |
y.backward(torch.eye(noutputs)) | |
return x.grad.data |
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from __future__ import print_function | |
import threading | |
from joblib import Parallel, delayed | |
import Queue | |
import os | |
# Fix print | |
_print = print | |
_rlock = threading.RLock() | |
def print(*args, **kwargs): |
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""" 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 |
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# Description: | |
# Joke commands. | |
# | |
# Commands: | |
# ぬるぽ - You reply with, "ガッ" When you post a "ぬるぽ" word. | |
# | |
# Notes: | |
# ネタ/ジョーク系のbot全般 | |
module.exports = (robot) -> |