Continual learning is a subfield of machine learning, which aims to allow machine learning
models to continuously learn on new data, by accumulating knowledge without forgetting …

Artificial Intelligence (AI) methods are powerful tools for various domains, including critical
fields such as avionics, where certification is required to achieve and maintain an …

Recent literature has proposed approaches that learn control policies with high performance
while maintaining safety guarantees. Synthesizing Hamilton-Jacobi (HJ) reachable sets has …

Temporal difference (TD) learning is often used to update the estimate of the value function
which is used by RL agents to extract useful policies. In this paper, we focus on value …

A key challenge in lifelong reinforcement learning (RL) is the loss of plasticity, where
previous learning progress hinders an agent's adaptation to new tasks. While regularization …

The Credit Assignment Problem (CAP) refers to the longstanding challenge of
Reinforcement Learning (RL) agents to associate actions with their long-term …

A lot of recent machine learning research papers have``open-ended learning''in their title.
But very few of them attempt to define what they mean when using the term. Even worse …

Modern reinforcement learning has been conditioned by at least three dogmas. The first is
the environment spotlight, which refers to our tendency to focus on modeling environments …

Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …

Current RLHF frameworks for aligning large language models (LLMs) typically assume a
fixed prompt distribution, which is sub-optimal and limits the scalability of alignment and …