Main

processing priority

3

site type

0 (generic, awaiting analysis)

review version

11

html import

20 (imported)

Events

first seen date

2025-02-08 02:13:50

expired found date

-

created at

2025-02-08 02:13:50

updated at

2025-02-08 02:13:51

Domain name statistics

length

18

crc

63678

tld

86

nm parts

0

nm random digits

0

nm rare letters

0

Connections

is subdomain of id

87719371 (github.io)

previous id

0

replaced with id

0

related id

-

dns primary id

0

dns alternative id

0

lifecycle status

0 (unclassified, or currently active)

Subdomains and pages

deleted subdomains

0

page imported products

0

page imported random

0

page imported parking

0

Error counters

count skipped due to recent timeouts on the same server IP

0

count content received but rejected due to 11-799

0

count dns errors

0

count cert errors

0

count timeouts

0

count http 429

0

count http 404

0

count http 403

0

count http 5xx

0

next operation date

-

Server

server bits

server ip

-

Mainpage statistics

mp import status

20

mp rejected date

-

mp saved date

-

mp size orig

36568

mp size raw text

23087

mp inner links count

0

mp inner links status

1 (no links)

Open Graph

title

E3B

description

Web publication for Exploration via Elliptical Episodic Bonuses.

image

og-image.png

site name

author

updated

2026-03-02 06:11:21

raw text

E3B Overview In recent years, a number of reinforcement learning (RL) methods have been proposed to explore complex environments which differ across episodes, such as procedurally-generated video games or embodied AI tasks. In this work, we show that the effectiveness of these methods critically relies on a count-based episodic term in their exploration bonus. As a result, despite their success in relatively simple, noise-free settings, these methods fall short in more realistic scenarios where the state space is vast and prone to noise. To address this limitation, we introduce Exploration via Elliptical Episodic Bonuses (E3B), a new method which extends count-based episodic bonuses to continuous state spaces and encourages an agent to explore states that are diverse under a learned embedding within each episode. The embedding is learned using an inverse dynamics model in order to capture controllable aspects of the environment. Our method sets a new state-of-the-art across 16 chal...

Text analysis

redirect type

0 (-)

block type

0 (no issues)

detected language

1 (English)

category id

Zastosowania AI (149)

index version

1

spam phrases

0

Text statistics

text nonlatin

0

text cyrillic

0

text characters

17984

text words

3524

text unique words

871

text lines

132

text sentences

136

text paragraphs

52

text words per sentence

25

text matched phrases

0

text matched dictionaries

0

RSS

rss path

rss status

1 (priority 1 already searched, no matches found)

rss found date

-

rss size orig

0

rss items

0

rss spam phrases

0

rss detected language

0 (awaiting analysis)

inbefore feed id

-

inbefore status

0 (new)

Sitemap

sitemap path

sitemap status

1 (priority 1 already searched, no matches found)

sitemap review version

2

sitemap urls count

0

sitemap urls adult

0

sitemap filtered products

0

sitemap filtered videos

0

sitemap found date

-

sitemap process date

-

sitemap first import date

-

sitemap last import date

-