Abstract Model-based reinforcement learning algorithms with probabilistic dynamical
models are amongst the most data-efficient learning methods. This is often attributed to their …

Robotic navigation in environments shared with other robots or humans remains
challenging because the intentions of the surrounding agents are not directly observable …

Current model-based reinforcement learning approaches use the model simply as a learned
black-box simulator to augment the data for policy optimization or value function learning. In …

We present two elegant solutions for modeling continuous-time dynamics, in a novel model-
based reinforcement learning (RL) framework for semi-Markov decision processes (SMDPs) …

Automated industries lead to high quality production, lower manufacturing cost and better
utilization of human resources. Robotic manipulator arms have major role in the automation …

Autonomous navigation in dense traffic scenarios remains challenging for autonomous
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …

Function approximation is widely used in reinforcement learning to handle the
computational difficulties associated with very large state spaces. However, function …

Nonlinear model predictive control (NMPC) is typically restricted to short, finite horizons to
limit the computational burden of online optimization. This makes a global planner …

Reinforcement Learning (RL) has advanced the state-of-the-art in many applications in the
last decade. The root of its success stems from having access to high-quality simulators …

Autonomous mobile robots navigating among humans must not only consider safety and
efficiency but also move acceptably in the current social context. A hybrid deep …