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

Low precision networks in the reinforcement learning (RL) setting arerelatively unexplored because of the limitations of binary activations forfunction approximation. Here, in the discrete action ATARI domain, wedemonstrate, for the first time, that low precision policy distillation from ahigh precision network provides a principled, practical way to train an RLagent. As an application, on 10 different ATARI games, we demonstrate real-timeend-to-end game playing on low-power neuromorphic hardware by converting asequence of game frames into discrete actions.

Low Precision Policy Distillation with Application to Low-Power, Real-time Sensation-Cognition-Action Loop with Neuromorphic Computing