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

Reinforcement learning has shown promise in learning policies that can solvecomplex problems. However, manually specifying a good reward function can bedifficult, especially for intricate tasks. Inverse reinforcement learningoffers a useful paradigm to learn the underlying reward function directly fromexpert demonstrations. Yet in reality, the corpus of demonstrations may containtrajectories arising from a diverse set of underlying reward functions ratherthan a single one. Thus, in inverse reinforcement learning, it is useful toconsider such a decomposition. The options framework in reinforcement learningis specifically designed to decompose policies in a similar light. We thereforeextend the options framework and propose a method to simultaneously recoverreward options in addition to policy options. We leverage adversarial methodsto learn joint reward-policy options using only observed expert states. We showthat this approach works well in both simple and complex continuous controltasks and shows significant performance increases in one-shot transferlearning.

OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning