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

We present pyRDDLGym, a Python framework for auto-generation of OpenAI Gymenvironments from RDDL declerative description. The discrete time stepevolution of variables in RDDL is described by conditional probabilityfunctions, which fits naturally into the Gym step scheme. Furthermore, sinceRDDL is a lifted description, the modification and scaling up of environmentsto support multiple entities and different configurations becomes trivialrather than a tedious process prone to errors. We hope that pyRDDLGym willserve as a new wind in the reinforcement learning community by enabling easyand rapid development of benchmarks due to the unique expressive power of RDDL.By providing explicit access to the model in the RDDL description, pyRDDLGymcan also facilitate research on hybrid approaches for learning from interactionwhile leveraging model knowledge. We present the design and built-in examplesof pyRDDLGym, and the additions made to the RDDL language that wereincorporated into the framework.

pyRDDLGym: From RDDL to Gym Environments