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https://krb-sjobs.brassring.com/TGnewUI/Search/home/HomeWithPreLoad?PageType=JobDetails&jobid=74414&partnerid=26059&siteid=5016&type=mail |
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Following the installation and first test for torch-twrl | |
https://github.com/twitter/torch-twrl | |
Run code: | |
cd examples | |
chmod u+x cartpole-pg.sh | |
./cartpole-pg.sh |
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from sklearn.feature_extraction.text import CountVectorizer | |
from collections import Counter | |
import numpy as np | |
with open('accepted-papers.txt', 'r') as myfile: | |
data=myfile.read().replace('\n', '') | |
lectures = data | |
bigram_vectorizer = CountVectorizer(ngram_range=(2, 10), stop_words='english') | |
analyze = bigram_vectorizer.build_analyzer() |
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""" | |
Translated from https://webdocs.cs.ualberta.ca/~sutton/MountainCar/MountainCar1.cp | |
Algorithm described at https://webdocs.cs.ualberta.ca/~sutton/book/ebook/node89.html | |
Some minor adjustments to constants were made to make the program work on environments | |
besides Mountain Car. | |
""" | |
import random | |
import math | |
import numpy as np |
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SONNETS | |
TO THE ONLY BEGETTER OF | |
THESE INSUING SONNETS | |
MR. W. H. ALL HAPPINESS | |
AND THAT ETERNITY | |
PROMISED BY | |
OUR EVER-LIVING POET WISHETH |
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hs.hotkey.bind({"cmd", "alt", "ctrl"}, "Left", function() | |
local win = hs.window.focusedWindow() | |
local f = win:frame() | |
local screen = win:screen() | |
local max = screen:frame() | |
f.x = max.x | |
f.y = max.y | |
f.w = max.w / 2 | |
f.h = max.h |
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""" | |
Policy Gradients | |
1. Sample paths. | |
2. Process paths (compute advantage, baseline, rewards, etc) | |
3. Run the paths through the policy (function approximator) | |
4. Compute gradients/update policy model weights | |
5. Profit?!?! | |
How we optimize the policy | |
-------------------------- | |
L(theta) = sum t=0 to T-1 log policy(action_t | state_t, theta) * A_t |
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Using the rllab TRPO algorithm |
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Using TRPO from https://github.com/rllab/rllab |
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Following https://github.com/rllab/rllab |