Reinforcement learning has received significant interest in recent years, due primarily to the
successes of deep reinforcement learning at solving many challenging tasks such as …

Lack of reliability is a well-known issue for reinforcement learning (RL) algorithms. This
problem has gained increasing attention in recent years, and efforts to improve it have …

Reinforcement learning (RL) has recently achieved tremendous success in solving complex
tasks. Careful considerations are made towards reproducible research in machine learning …

This article focuses on the recent advances in the field of reinforcement learning (RL) as well
as the present state–of–the–art applications in games. First, we give a general panorama of …

Novel reinforcement learning algorithms, or improvements on existing ones, are commonly
justified by evaluating their performance on benchmark environments and are compared to …

Reinforcement learning is a wide ranging subfield of machine learning, which has been
brought to the forefront of research after the unprecedented rise of deep learning. The …

Performance evaluations are critical for quantifying algorithmic advances in reinforcement
learning. Recent reproducibility analyses have shown that reported performance results are …

The abstract for the paper titled" Advancements in Reinforcement Learning: From Theory to
Real-World Applications" outlines the core focus and highlights of the study. Abstract …

Empirical design in reinforcement learning is no small task. Running good experiments
requires attention to detail and at times significant computational resources. While compute …

Reinforcement Learning (RL) is one of the most dynamic research areas in Game AI and AI
as a whole, and a wide variety of games are used as its prominent test problems. However, it …