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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Reinforcement learning (RL) has been widely applied to game-playing andsurpassed the best human-level performance in many domains, yet there are fewuse-cases in industrial or commercial settings. We introduce OR-Gym, anopen-source library for developing reinforcement learning algorithms to addressoperations research problems. In this paper, we apply reinforcement learning tothe knapsack, multi-dimensional bin packing, multi-echelon supply chain, andmulti-period asset allocation model problems, as well as benchmark the RLsolutions against MILP and heuristic models. These problems are used inlogistics, finance, engineering, and are common in many business operationsettings. We develop environments based on prototypical models in theliterature and implement various optimization and heuristic models in order tobenchmark the RL results. By re-framing a series of classic optimizationproblems as RL tasks, we seek to provide a new tool for the operations researchcommunity, while also opening those in the RL community to many of the problemsand challenges in the OR field.

OR-Gym: A Reinforcement Learning Library for Operations Research Problems