REVIEW 1 cited by
Crawling in Rogue's dungeons with (partitioned) A3C
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Rogue is a famous dungeon-crawling video-game of the 80ies, the ancestor of its gender. Rogue-like games are known for the necessity to explore partially observable and always different randomly-generated labyrinths, preventing any form of level replay. As such, they serve as a very natural and challenging task for reinforcement learning, requiring the acquisition of complex, non-reactive behaviors involving memory and planning. In this article we show how, exploiting a version of A3C partitioned on different situations, the agent is able to reach the stairs and descend to the next level in 98% of cases.
Forward citations
Cited by 1 Pith paper
-
Evolutionary reinforcement learning of dynamical large deviations
An evolutionary algorithm that mutates a reference model's rates or neural-network weights produces tight upper bounds on dynamical large-deviation rate functions, matching exact results on three test models.
Discussion (0). Continue with ORCID to comment.