We present a hierarchical planning system and its application to robotic manipulation. The
novel features of the system are: 1) it finds high-quality kinematic solutions to task-level …

We present an algorithm, HI-MAT (Hierarchy Induction via Models And Trajectories), that
discovers MAXQ task hierarchies by applying dynamic Bayesian network models to a …

Robots acting in human-scale environments must plan under uncertainty in large state–
action spaces and face constantly changing reward functions as requirements and goals …

Singapore's vision of a Smart Nation encompasses the development of effective and efficient
means of transportation. The government's target is to leverage new technologies to create …

Hierarchical decomposition promises to help scale reinforcement learning algorithms
naturally to real-world problems by exploiting their underlying structure. Model-based …

Markov decision processes (MDPs) provide a rich framework for planning under uncertainty.
However, exactly solving a large MDP is usually intractable due to the “curse of …

A novel method was designed to solve reinforcement learning problems with artificial
potential field. Firstly a reinforcement learning problem was transferred to a path planning …

Reinforcement learning is learning from interaction with an environment to achieve a goal. It
is an efficient framework to solve sequential decision-making problems, using Markov …

Reinforcement learning (RL) allows an intelligent agent to learn optimal behavior as it
interacts with its environment. Conventional model-based RL algorithms learn rapidly, but …

Hierarchical task decompositions play an essential role in the design of complex simulation
and decision systems, such as the ones that arise in video games. Game designers find it …