… -learning learning algorithms) in AI-GAs [7]. However, we aim to achieve generalisation
not just across tasks but also across different domains. Learning domain-invariant algorithms …

… supply chain drivers, algorithms, data sources, … learning algorithm is still the most popular
one. Second, inventory management is the most common application of reinforcement learning …

… Reinforcement learning … reinforcement learning techniques for tackling dynamically changing
environment contexts in a system. The focus is on a single autonomous RL agent learning …

… We give an overview of the recent advances in deep reinforcement learning algorithms for
… of reinforcement learning and the parts of a reinforcement learning system. The many deep …

… Reinforcement learning is of great … algorithms of reinforcement learning that build on the
powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning …

… for meta-learning reinforcement learning algorithms by … algorithms are domain-agnostic
and can generalize to new environments not seen during training. Our method can both learn …

… In recent years, Deep Reinforcement Learning (DRL) algorithms have achieved great suc…
Nevertheless, understanding all the implementation details of an algorithm remains difficult …

… and use of Deep Learning(DL), RL, and Deep Reinforcement Learning (DRL)methods …
Reinforcement learning and deep reinforcement learning and how deep reinforcement learning …

… of cooperative multi-agent learning tasks. Our experiments serve as a … algorithms across
different learning tasks, and we provide insights regarding the effectiveness of different learning …

… quantifying algorithmic advances in reinforcement learning. … methodology for reinforcement
learning algorithms that … broad class of reinforcement learning algorithms on standard …