Devdiscourse

AI trading systems mimicking human bias show higher risk

Reinforcement learning frames trading as a sequential decision-making problem, where an agent observes market conditions, ...

Leadership Execution Institute Defines Evaluation Framework Explaining Why Training Cannot Eliminate Supervisor Drift

Independent analysis explains why episodic leadership training fails to sustain behavioral consistency and introduces an execution system evaluation framework. Traditional leadership training fails ...
IEEE

A Novel RMS-Driven Deep Reinforcement Learning for Optimized Portfolio Management in Stock Trading

Abstract: Algorithmic stock trading has improved tremendously, with Reinforcement Learning (RL) algorithms being more adaptable than classic approaches like mean reversion and momentum. However, ...
GitHub

Demystifying Reinforcement Learning in Agentic Reasoning

An overview of our research on agentic RL. In this work, we systematically investigate three dimensions of agentic RL: data, algorithms, and reasoning modes. Our findings reveal: Real end-to-end ...
IEEE

Toward Energy-Efficient Spike-Based Deep Reinforcement Learning With Temporal Coding

Abstract: Deep reinforcement learning (DRL) facilitates efficient interaction with complex environments by enabling continuous optimization strategies and providing agents with autonomous learning ...
Optometry Advisor

Negative Reinforcement Linked to Compulsive Behavior in Chronic Opioid Use

Individuals with chronic opioid use, whether addicted or not, show heightened learning from negative reinforcement, suggesting that avoidance behavior may underlie both the development and persistence ...