This article is a gentle discussion about the field of reinforcement learning in practice, about
opportunities and challenges, touching a broad range of topics, with perspectives and …

A wide range of reinforcement learning (RL) algorithms have been proposed, in which
agents learn from interactions with a simulated environment. Executing such RL training …

Parallel Reinforcement Learning (RL) frameworks are essential for mapping RL workloads
to multiple computational resources, allowing for faster generation of samples, estimation of …

Reinforcement Learning from Human Feedback (RLHF) is widely used in Large Language
Model (LLM) alignment. Traditional RL can be modeled as a dataflow, where each node …

As use of data driven technologies spreads, software engineers are more often faced with
the task of solving a business problem using data-driven methods such as machine learning …

AI integration is revolutionizing the landscape of HPC simulations, enhancing the
importance, use, and performance of AI-driven HPC workflows. This paper surveys the …

Deep Reinforcement Learning (DRL) achieves great success in various domains.
Communication in today's DRL algorithms takes non-negligible time compared to the …

Knowledge graphs are at the core of numerous consumer and enterprise applications where
learned graph embeddings are used to derive insights for the users of these applications …

Reinforcement Learning (RL) has marked significant achievements across a variety of
complex tasks in real-world scenarios. However, the efficacy of RL predominantly relies on …

Traffic simulation has gained a lot of interest for massive safety evaluation of self-driving
systems in a risk free setting but the reality gap remains a big challenge. Adversarial …