Abstract: Reinforcement ... inference in RL algorithms is essential for understanding and managing uncertainty in model performance. This paper introduces a generalized framework for time-varying ...

In order to ensure the prediction accuracy of mechanical properties, a TCMSSA-ELM model, which is a hybrid of the sparrow search algorithm (SSA) optimized by the tent chaotic mapping (TCM) algorithm ...

Now, fueled by the remarkable advancements in reinforcement learning (RL), this vision is rapidly becoming our reality. The recent Turing Award, the highest honor in computer science, recognized ...

Pamela Weber Harris , a former high school math teacher contends in a new volume, Developing Mathematical Reasoning: Avoiding ...

An algorithm called time–magnitude reinforcement learning (TMRL) extends distributional reinforcement learning to take account of reward time and magnitude, and behavioural and ...

This is a potentially valuable modeling study on sequence generation in the hippocampus in a variety of behavioral contexts. While the scope of the model is ambitious, its presentation is incomplete ...

Researchers have identified peptides that can help remove microplastics from the environment by combining biophysical modeling, molecular dynamics, quantum computing, and reinforcement learning.

Grammarly incorporates both machine learning and rule-based algorithms into its products. No computational linguists, however, labored over imparting our rules of language to OpenAI’s GPT-2.

The three-dimensional world of ordinary experience—the universe filled with galaxies, stars, planets, houses, boulders, and ...