MetaRL pre-trained on GBWM problems delivers near-optimal dynamic strategies in 0.01s achieving 97.8% of DP optimal utility and handles larger problems where DP fails.
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2 Pith papers cite this work. Polarity classification is still indexing.
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ReCAP segments markets into regimes, builds a policy library via continual learning, and uses a regime-gate to adapt trading policies, claiming superior returns and fast adaptation on five real datasets.
citing papers explorer
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A Meta Reinforcement Learning Approach to Goals-Based Wealth Management
MetaRL pre-trained on GBWM problems delivers near-optimal dynamic strategies in 0.01s achieving 97.8% of DP optimal utility and handles larger problems where DP fails.
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Regime-Adaptive Continual Learning for Portfolio Management
ReCAP segments markets into regimes, builds a policy library via continual learning, and uses a regime-gate to adapt trading policies, claiming superior returns and fast adaptation on five real datasets.