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 capability of a reinforcement learning (RL) agent heavily depends on thediversity of the learning scenarios generated by the environment. Generation ofdiverse realistic scenarios is challenging for real-time strategy (RTS)environments. The RTS environments are characterized by intelligententities/non-RL agents cooperating and competing with the RL agents with largestate and action spaces over a long period of time, resulting in an infinitespace of feasible, but not necessarily realistic, scenarios involving complexinteraction among different RL and non-RL agents. Yet, most of the existingsimulators rely on randomly generating the environments based on predefinedsettings/layouts and offer limited flexibility and control over the environmentdynamics for researchers to generate diverse, realistic scenarios as per theirdemand. To address this issue, for the first time, we formally introduce thebenefits of adopting an existing formal scenario specification language,SCENIC, to assist researchers to model and generate diverse scenarios in an RTSenvironment in a flexible, systematic, and programmatic manner. To showcase thebenefits, we interfaced SCENIC to an existing RTS environment Google ResearchFootball(GRF) simulator and introduced a benchmark consisting of 32 realisticscenarios, encoded in SCENIC, to train RL agents and testing theirgeneralization capabilities. We also show how researchers/RL practitioners canincorporate their domain knowledge to expedite the training process byintuitively modeling stochastic programmatic policies with SCENIC.

Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments