Recent decades have seen a rise in the use of physics methods to study different societal
phenomena. This development has been due to physicists venturing outside of their …

Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …

Abstract Proximal Policy Optimization (PPO) is a ubiquitous on-policy reinforcement learning
algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent …

The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …

While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

We present Theseus, an efficient application-agnostic open source library for differentiable
nonlinear least squares (DNLS) optimization built on PyTorch, providing a common …

In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep
learning. This field of research has been able to solve a wide range of complex …

Reinforcement learning (RL) algorithms have been around for decades and employed to
solve various sequential decision-making problems. These algorithms, however, have faced …