id
related bits
0
processing priority
3
site type
5 (wiki-type site, growing by topic rather than chronologically)
review version
11
html import
20 (imported)
first seen date
2024-09-15 06:05:27
expired found date
-
created at
2024-09-15 06:05:27
updated at
2024-11-01 13:50:01
length
20
crc
31263
tld
86
nm parts
0
nm random digits
0
nm rare letters
0
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)
deleted subdomains
0
page imported products
0
page imported random
0
page imported parking
0
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 bits
—
server ip
-
mp import status
20
mp rejected date
-
mp saved date
-
mp size orig
53503
mp size raw text
31503
mp inner links count
1
mp inner links status
10 (links queued, awaiting import)
title
Evolving Curricula
description
Interactive publication for the paper Evolving Curricula with Regret-Based Environment Design.
image
site name
author
updated
2026-03-06 21:13:26
raw text
Evolving Curricula Overview We evolve environments at the frontier of a reinforcement learning agent's capabilities, leading to self-supervised teacher-student processes with strong zero-shot generalization results for agents learning to walk through challenging terrain and navigating complex human-designed mazes. Roughness 1 Stump height 1 - 3 Pit gap 3 - 5 Stair steps 3 - 5 Run Reset Show all seeds Interactive demo. Design your own challenging levels on which to compare an ACCEL agent to baseline methods. Introduction Deep reinforcement learning (RL) has seen tremendous success over the past decade. However, agents trained on fixed environments are brittle, often failing the moment the environment changes even slightly, thus limiting the real-world applicability of current RL methods. A common remedy is to introduce more training data diversity by randomizing the environment’s parameters in every episode—a process called domain randomization (...
redirect type
0 (-)
block type
0 (no issues)
detected language
1 (English)
category id
index version
1
spam phrases
0
text nonlatin
0
text cyrillic
0
text characters
24902
text words
4675
text unique words
1174
text lines
243
text sentences
207
text paragraphs
63
text words per sentence
22
text matched phrases
0
text matched dictionaries
0
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 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
-