1 citations · 1 across the 5 of their papers we have counts for
5 papers
Self-Supervised Auxiliary Task Discovery for Stable Reinforcement Learning in Stock Trading
Arishi Orra, Himanshu Choudhary, Manoj Thakur
Reinforcement learning has gained increasing attention as a data-driven approach for stock trading. However, learning a policy that is both profitable and stable remains challengin…
Learning Stock Trading Policies via Barycenter-Based Adversarial Inverse Reinforcement Learning
Arishi Orra, Himanshu Choudhary, Manoj Thakur
Designing effective trading strategies using reinforcement learning remains challenging due to delayed and noisy rewards, poor exploration, and the difficulty of enforcing explicit…
Diffusion-Augmented Reinforcement Learning for Robust Portfolio Optimization under Stress Scenarios
Himanshu Choudhary, Arishi Orra, Manoj Thakur
In the ever-changing and intricate landscape of financial markets, portfolio optimisation remains a formidable challenge for investors and asset managers. Conventional methods ofte…
FinXplore: An Adaptive Deep Reinforcement Learning Framework for Balancing and Discovering Investment Opportunities
Himanshu Choudhary, Arishi Orra, Manoj Thakur
Portfolio optimization is essential for balancing risk and return in financial decision-making. Deep Reinforcement Learning (DRL) has stood out as a cutting-edge tool for portfolio…
Deep Reinforcement Learning for Investor-Specific Portfolio Optimization: A Volatility-Guided Asset Selection Approach
Arishi Orra, Aryan Bhambu, Himanshu Choudhary +2
Portfolio optimization requires dynamic allocation of funds by balancing the risk and return tradeoff under dynamic market conditions. With the recent advancements in AI, Deep Rein…