In recent years, reinforcement learning (RL) has achieved remarkable success due to the
growing adoption of deep learning techniques and the rapid growth of computing power …

Abstract Monte-Carlo Tree Search, especially UCT and its POMDP version POMCP, have
demonstrated excellent performanceon many problems. However, to efficiently scale to …

Reinforcement Learning (RL) has emerged as a strong approach in the field of Artificial
intelligence, specifically, in the field of machine learning, robotic navigation, etc. In this paper …

Partially observable semi-Markov decision processes (POSMDPs) provide a rich framework
for planning under both state transition uncertainty and observation uncertainty. In this …

We propose a model-based approach to hierarchical reinforcement learning that exploits
shared knowledge and selective execution at different levels of abstraction, to efficiently …

Efficient object manipulation based only on force feedback typically requires a plan of
actively contact-seeking actions to reduce uncertainty over the true environmental model. In …

Partial observability, or the inability of an agent to fully observe the state of its environment,
exists in many real-world problem domains. However, most cognitive architectures do not …

Motion planning in uncertain environments is a common challenge and essential for
autonomous robot operations. Representatively, the determinized sparse partially …

In reinforcement learning (RL), the exploration/exploitation (E/E) dilemma is a very crucial
issue, which can be described as searching between the exploration of the environment to …

Reinforcement learning (RL) is an area of machine learning that is concerned with how an
agent learns to make decisions sequentially in order to optimize a particular performance …