Reinforcement learning (RL) is a machine learning approach that trains agents to maximize
cumulative rewards through interactions with environments. The integration of RL with deep …

Determining how environments shape how people learn is central to understanding
individual differences in goal-directed behaviour. Studies of the effects of early-life adversity …

While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

In order to improve reproducibility, deep reinforcement learning (RL) has been adopting
better scientific practices such as standardized evaluation metrics and reporting. However …

Tremendous progress has been made in reinforcement learning (RL) over the past decade.
Most of these advancements came through the continual development of new algorithms …

Reinforcement learning (RL) algorithms update an agent's parameters according to one of
several possible rules, discovered manually through years of research. Automating the …

Many important real-world problems have action spaces that are high-dimensional,
continuous or both, making full enumeration of all possible actions infeasible. Instead, only …

Due to the recent advancements in the Internet of Things and data science techniques, a
wide range of studies have investigated the use of data mining (DM) and machine learning …

Genetic algorithms constitute a family of black-box optimization algorithms, which take
inspiration from the principles of biological evolution. While they provide a general-purpose …

Meta-learning empowers artificial intelligence to increase its efficiency by learning how to
learn. Unlocking this potential involves overcoming a challenging meta-optimisation …