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Online learning of a robot’s inverse dynamics model for trajectory tracking necessitates an interaction between the robot and its …

Hierarchical reinforcement learning methods offer a powerful means of planning flexible behavior in complicated domains. However, …

We propose an architecture as a robot’s decision-making mechanism to anticipate a human’s state of mind, and so plan …

A key challenge in many reinforcement learning problems is delayed rewards, which can significantly slow down learning. Although reward …

An important step in the construction of novel objects is the ability to recognise combinations of raw materials which are likely to be …

Current structure learning practices in Bayesian networks have been developed to learn the structure between observable variables and …

The high variability of fingerprint data (owing to, e.g., differences in quality, moisture conditions, and scanners) makes the task of …

Applying reinforcement learning to robotic systems poses a number of challenging problems. A key requirement is the ability to handle …

Hierarchical architectures are critical to the scalability of reinforcement learning methods. Most current hierarchical frameworks …

Monte Carlo Tree Search (MCTS) is a family of directed search algorithms that has gained widespread attention in recent years. Despite …