English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The Fighting Game AI Competition (FTGAIC) provides a challenging benchmarkfor 2-player video game AI. The challenge arises from the large action space,diverse styles of characters and abilities, and the real-time nature of thegame. In this paper, we propose a novel algorithm that combines Rolling HorizonEvolution Algorithm (RHEA) with opponent model learning. The approach isreadily applicable to any 2-player video game. In contrast to conventionalRHEA, an opponent model is proposed and is optimized by supervised learningwith cross-entropy and reinforcement learning with policy gradient andQ-learning respectively, based on history observations from opponent. The modelis learned during the live gameplay. With the learned opponent model, theextended RHEA is able to make more realistic plans based on what the opponentis likely to do. This tends to lead to better results. We compared our approachdirectly with the bots from the FTGAIC 2018 competition, and found our methodto significantly outperform all of them, for all three character. Furthermore,our proposed bot with the policy-gradient-based opponent model is the only onewithout using Monte-Carlo Tree Search (MCTS) among top five bots in the 2019competition in which it achieved second place, while using much less domainknowledge than the winner.

Enhanced Rolling Horizon Evolution Algorithm with Opponent Model Learning: Results for the Fighting Game AI Competition