Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs)
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …

Policy Mirror Descent (PMD) is a popular framework in reinforcement learning, serving as a
unifying perspective that encompasses numerous algorithms. These algorithms are derived …

Often times in imitation learning (IL), the environment we collect expert demonstrations in
and the environment we want to deploy our learned policy in aren't exactly the same (eg …

Reinforcement learning (RL) agents are commonly trained and evaluated in the same
environment. In contrast, humans often train in a specialized environment before being …

While reinforcement learning (RL) holds great potential for decision making in the real world,
it suffers from a number of unique difficulties which often need specific consideration. In …

Offline preference optimization is a key method for enhancing and controlling the quality of
Large Language Model (LLM) outputs. Typically, preference optimization is approached as …

Intrinsic motivation (IM) and reward shaping are common methods for guiding the
exploration of reinforcement learning (RL) agents by adding pseudo-rewards. Designing …

Combinatorial Optimization is crucial to numerous real-world applications, yet still presents
challenges due to its (NP-) hard nature. Amongst existing approaches, heuristics often offer …

Human intelligence emerged through the process of natural selection and evolution on
Earth. We investigate what it would take to re-create this process in silico. While past work …

Due to their simplicity and support of high levels of parallelism, evolutionary algorithms have
regained popularity in machine learning applications such as curriculum generation for …