Since the beginning of the 21st century, artificial intelligence (AI) has been reshaping almost
all areas of human society, which has high potential to spark the fourth industrial revolution …

While the maximum entropy (MaxEnt) reinforcement learning (RL) framework--often touted
for its exploration and robustness capabilities--is usually motivated from a probabilistic …

Abstract Offline Reinforcement Learning (RL) has demonstrated promising results in various
applications by learning policies from previously collected datasets, reducing the need for …

Many important real-world Reinforcement Learning (RL) problems involve partial
observability and require policies with memory. Unfortunately, standard deep RL algorithms …

Partially Observable Markov Decision Processes (POMDPs) are used to model
environments where the full state cannot be perceived by an agent. As such the agent needs …

Dual-arm robots offer enhanced versatility and efficiency over single-arm counterparts by
enabling concurrent manipulation of multiple objects or cooperative execution of tasks using …

Reinforcement learning (RL) has achieved phenomenal success in various domains.
However, its data-driven nature also introduces new vulnerabilities that can be exploited by …

Learning a good history representation is one of the core challenges of reinforcement
learning (RL) in partially observable environments. Recent works have shown the …

Reinforcement learning (RL) has made significant progress in areas such as Atari games
and robotic control, where the agents have perfect sensing capabilities. However, in many …

In recent years, penetration testing (pen‐testing) has emerged as a crucial process for
evaluating the security level of network infrastructures by simulating real‐world cyber …