arXiv (OAI Expanded)
open access
Learning Stock Trading Policies via Barycenter-Based Adversarial Inverse Reinforcement Learning
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Learning Stock Trading Policies via Barycenter-Based Adversarial Inverse Reinforcement Learning#616909
- KellyBoost: Growth-Optimal Portfolio Construction with Gradient-Boosted Trees#482124
- QUBO-Compatible Active Learning for Inverse Design of High-Entropy Alloys#784989
- QUBO-Compatible Active Learning for Inverse Design of High-Entropy Alloys#783399
- Improving Bayesian Optimization for Portfolio Management with an Adaptive Scheduling#179147
Record · ID 616409
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive