We study the reinforcement learning problem of complex action control in the Multi-player
Online Battle Arena (MOBA) 1v1 games. This problem involves far more complicated state …

MOBA games, eg, Honor of Kings, League of Legends, and Dota 2, pose grand challenges
to AI systems such as multi-agent, enormous state-action space, complex action control, etc …

We present JueWu-SL, the first supervised-learning-based artificial intelligence (AI) program
that achieves human-level performance in playing multiplayer online battle arena (MOBA) …

We aim to develop off-policy DRL algorithms that not only exceed state-of-the-art
performance but are also simple and minimalistic. For standard continuous control …

Abstract Deep Deterministic Policy Gradient (DDPG) algorithm is one of the most well-known
reinforcement learning methods. However, this method is inefficient and unstable in practical …

Policy gradient methods can solve complex tasks but often fail when the dimensionality of
the action-space or objective multiplicity grow very large. This occurs, in part, because the …

Reinforcement learning encounters formidable challenges when tasked with intricate
decision-making scenarios, primarily due to the expansive parameterized action spaces and …

The field of Deep Reinforcement Learning (DRL) has recently seen a surge in the popularity
of maximum entropy reinforcement learning algorithms. Their popularity stems from the …

Sample efficiency in deep reinforcement learning (DRL) is measured by the amount of new
data collected to learn a task. It is one of the most important research topics in DRL …

Action spaces equipped with parameter sets are a common occurrence in reinforcement
learning applications. Solutions to problems of this class have been developed under …