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

Sequential reasoning is a complex human ability, with extensive previousresearch focusing on gaming AI in a single continuous game, round-baseddecision makings extending to a sequence of games remain less explored.Counter-Strike: Global Offensive (CS:GO), as a round-based game with abundantexpert demonstrations, provides an excellent environment for multi-playerround-based sequential reasoning. In this work, we propose a Sequence Reasonerwith Round Attribute Encoder and Multi-Task Decoder to interpret the strategiesbehind the round-based purchasing decisions. We adopt few-shot learning tosample multiple rounds in a match, and modified model agnostic meta-learningalgorithm Reptile for the meta-learning loop. We formulate each round as amulti-task sequence generation problem. Our state representations combineaction encoder, team encoder, player features, round attribute encoder, andeconomy encoders to help our agent learn to reason under this specificmulti-player round-based scenario. A complete ablation study and comparisonwith the greedy approach certify the effectiveness of our model. Our researchwill open doors for interpretable AI for understanding episodic and long-termpurchasing strategies beyond the gaming community.

Learning to Reason in Round-based Games: Multi-task Sequence Generation for Purchasing Decision Making in First-person Shooters