Rolling Horizon NEAT for General Video Game Playing
- URL: http://arxiv.org/abs/2005.06764v1
- Date: Thu, 14 May 2020 07:25:23 GMT
- Title: Rolling Horizon NEAT for General Video Game Playing
- Authors: Diego Perez-Liebana, Muhammad Sajid Alam, Raluca D. Gaina
- Abstract summary: This paper presents a new Statistical Forward Planning (SFP) method, Rolling Horizon NeuroEvolution of Augmenting Topologies (rhNEAT)
Unlike traditional Rolling Horizon Evolution, rhNEAT evolves weights and connections of a neural network in real-time, planning several steps ahead before returning an action to execute in the game.
- Score: 1.160208922584163
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper presents a new Statistical Forward Planning (SFP) method, Rolling
Horizon NeuroEvolution of Augmenting Topologies (rhNEAT). Unlike traditional
Rolling Horizon Evolution, where an evolutionary algorithm is in charge of
evolving a sequence of actions, rhNEAT evolves weights and connections of a
neural network in real-time, planning several steps ahead before returning an
action to execute in the game. Different versions of the algorithm are explored
in a collection of 20 GVGAI games, and compared with other SFP methods and
state of the art results. Although results are overall not better than other
SFP methods, the nature of rhNEAT to adapt to changing game features has
allowed to establish new state of the art records in games that other methods
have traditionally struggled with. The algorithm proposed here is general and
introduces a new way of representing information within rolling horizon
evolution techniques.
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