RAPO uses a dual robust RL formulation with trajectory-level adversarial networks and model-level Boltzmann reweighting over dynamics ensembles to improve policy resilience and out-of-distribution generalization while keeping the problem tractable.
arXiv preprint arXiv:2011.09607v2 , year=
4 Pith papers cite this work. Polarity classification is still indexing.
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SBCA is a reinforcement learning framework using BERT cross-modal fusion and Actor-Critic to integrate price data with sentiment text for multi-asset portfolio optimization with practical trading constraints.
SSAI maps news into four factors (sentiment, risk, confidence, volatility) for trading, but factor portfolios, ridge models, and RL agents show no reliable edge over baselines after coverage controls and costs.
EvoNash-MARL achieves 19.6% annualized returns on equity allocation from 2014-2024 versus 11.7% for SPY, with evidence of robustness under constraints but no strong statistical superiority per WRC and SPA-lite tests.
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EvoNash-MARL: A Closed-Loop Multi-Agent Reinforcement Learning Framework for Medium-Horizon Equity Allocation
EvoNash-MARL achieves 19.6% annualized returns on equity allocation from 2014-2024 versus 11.7% for SPY, with evidence of robustness under constraints but no strong statistical superiority per WRC and SPA-lite tests.