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

Many transfer problems require re-using previously optimal decisions forsolving new tasks, which suggests the need for learning algorithms that canmodify the mechanisms for choosing certain actions independently of those forchoosing others. However, there is currently no formalism nor theory for how toachieve this kind of modular credit assignment. To answer this question, wedefine modular credit assignment as a constraint on minimizing the algorithmicmutual information among feedback signals for different decisions. We introducewhat we call the modularity criterion for testing whether a learning algorithmsatisfies this constraint by performing causal analysis on the algorithmitself. We generalize the recently proposed societal decision-making frameworkas a more granular formalism than the Markov decision process to prove that fordecision sequences that do not contain cycles, certain single-step temporaldifference action-value methods meet this criterion while all policy-gradientmethods do not. Empirical evidence suggests that such action-value methods aremore sample efficient than policy-gradient methods on transfer problems thatrequire only sparse changes to a sequence of previously optimal decisions.

Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment