Local Optimization in Monte-Carlo Tree Search for the Traveling Salesman Problem
Published:
Enhancement of Monte-Carlo Tree Search for the Traveling Salesman Problem with 2-opt Optimization
Download here
Download the French version here
A variety of common markup showing how the theme styles them.
Single line blockquote:
Quotes are cool.
Entry | Item | |
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John Doe | 2016 | Description of the item in the list |
Jane Doe | 2019 | Description of the item in the list |
Doe Doe | 2022 | Description of the item in the list |
Header1 | Header2 | Header3 |
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cell1 | cell2 | cell3 |
cell4 | cell5 | cell6 |
cell1 | cell2 | cell3 |
cell4 | cell5 | cell6 |
Foot1 | Foot2 | Foot3 |
Make any link standout more when applying the .btn
class.
Watch out! You can also add notices by appending {: .notice}
to a paragraph.
This is an example of a link.
The abbreviation CSS stands for “Cascading Style Sheets”.
“Code is poetry.” —Automattic
You will learn later on in these tests that word-wrap: break-word;
will be your best friend.
This tag will let you strikeout text.
The emphasize tag should italicize text.
This tag should denote inserted text.
This scarcely known tag emulates keyboard text, which is usually styled like the <code>
tag.
This tag styles large blocks of code.
.post-title { margin: 0 0 5px; font-weight: bold; font-size: 38px; line-height: 1.2; and here's a line of some really, really, really, really long text, just to see how the PRE tag handles it and to find out how it overflows; }
Developers, developers, developers…
–Steve Ballmer
This tag shows bold text.
Getting our science styling on with H2O, which should push the “2” down.
Still sticking with science and Isaac Newton’s E = MC2, which should lift the 2 up.
This allows you to denote variables.
Published:
Enhancement of Monte-Carlo Tree Search for the Traveling Salesman Problem with 2-opt Optimization
Download here
Download the French version here
Published:
Inspired by satisficing, we introduce a novel concept of non-maximizing agents, א-aspiring agents, whose goal is to achieve an expected gain of א. We derive aspiration-based algorithms from Q-learning and DQN.
Download here
Published:
A Python package for analyzing and interpreting transformer model behaviors through activation analysis and interventions, based on nnsight
Published:
We provide causal evidence for the existence of language-agnostic concept representations within LLMs
Download here
Published:
We investigate the differences between base and instruction-tuned language models using Crosscoders
Published:
A minimal, hackable package for building feature activation dashboards in transformer models
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