… -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 …

… In this paper, we firstly survey the model and theory of reinforcement learning. Then, we
roundly present the main reinforcement learning algorithms, including Sarsa, … Q-learning …

… , such as learning to play video games just from pixel information, are now successfully solved
using deep reinforcement learning. … model-free RL algorithms and pathbreaking function …

… 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 …

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

… of reinforcement learning from a computer-science perspective. It is written to be accessible
to researchers familiar with machine learning… -free algorithms for reinforcement learning from …

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

… Temporal difference learning algorithms are fundamental for evaluating/predicting value …
Control algorithms find optimal policies. Reinforcement learning algorithms may be based on …

… 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 Reinforcement Learning algorithms are briefly discussed. Reinforcement Learning can be
… Nowadays, Meta-Learning, Automated Machine Learning and Self-Learning Systems have …