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.
Finrl: A deep reinforcement learning library for automated stock trading in quantitative finance.CoRR, abs/2011.09607
10 Pith papers cite this work. Polarity classification is still indexing.
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Counterfactual transport flows enable conservative, instance-specific trajectory refinement in offline RL by constructing local preference pairs in latent space from offline data and learning refinement directions controlled by a strength parameter.
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.
FPQC-SAC adds a bounded parameterized quantum circuit to SAC to constrain representations in low-SNR financial environments, reporting 66.89% higher cumulative returns than standard SAC on real portfolio tasks.
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.
An empirical literature analysis reveals a bifurcation in RL environments into Semantic Prior (LLM-dominated) and Domain-Specific Generalization ecosystems with distinct cognitive fingerprints.
A hybrid DRL system for multi-pair crypto trading with deterministic risk shielding outperforms a heuristic baseline at 10% significance on Binance futures data.
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.
AlphaQuanter introduces a single-agent tool-augmented RL framework for stock trading that learns dynamic policies over a transparent decision workflow and reports state-of-the-art financial metrics.
A review paper that builds a taxonomy of AI methods (supervised, unsupervised, reinforcement learning, NLP, optimization) and a framework for their use in ESG score prediction, controversy detection, portfolio management, and sustainability report analysis.
citing papers explorer
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Robust Adversarial Policy Optimization Under Dynamics Uncertainty
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.
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Counterfactual Transport Flows for Offline Conservative Trajectory Refinement
Counterfactual transport flows enable conservative, instance-specific trajectory refinement in offline RL by constructing local preference pairs in latent space from offline data and learning refinement directions controlled by a strength parameter.
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SBCA: Cross-Modal BERT-driven Actor-Critic for Multi-Asset Portfolio Optimization
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.
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Mitigating Bias in Low-SNR Financial Reinforcement Learning via Quantum Representations
FPQC-SAC adds a bounded parameterized quantum circuit to SAC to constrain representations in low-SNR financial environments, reporting 66.89% higher cumulative returns than standard SAC on real portfolio tasks.
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Semantic State Abstraction Interfaces for LLM-Augmented Portfolio Decisions: Multi-Axis News Decomposition and RL Diagnostics
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.
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From Pixels to Digital Agents: An Empirical Study on the Taxonomy and Technological Trends of Reinforcement Learning Environments
An empirical literature analysis reveals a bifurcation in RL environments into Semantic Prior (LLM-dominated) and Domain-Specific Generalization ecosystems with distinct cognitive fingerprints.
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Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning
A hybrid DRL system for multi-pair crypto trading with deterministic risk shielding outperforms a heuristic baseline at 10% significance on Binance futures data.
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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.
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AI-Powered Sustainable Finance: An Integrative Taxonomy and Framework of AI Applications for Sustainable Investment Decision-Making
A review paper that builds a taxonomy of AI methods (supervised, unsupervised, reinforcement learning, NLP, optimization) and a framework for their use in ESG score prediction, controversy detection, portfolio management, and sustainability report analysis.