Apr 20
Multi-objective Reinforcement Learning With Augmented States Requires Rewards After Deployment
★★★★★
significance 2/5
This research paper identifies a critical requirement in multi-objective reinforcement learning (MORL) regarding augmented states. It explains that using augmented states necessitates continued access to reward signals even after a model has been deployed.
Why it matters
Deployment strategies for complex multi-objective systems must account for the necessity of continuous reward signal access to maintain performance.
Tags
#reinforcement learning #morl #augmented states #deploymentRelated coverage
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