Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …

Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …

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 …

While large language models (LLMs) have demonstrated impressive performance on a
range of decision-making tasks, they rely on simple acting processes and fall short of broad …

Deep reinforcement learning (DRL) has made great achievements since proposed.
Generally, DRL agents receive high-dimensional inputs at each step, and make actions …

We consider deep policy learning with only batched historical trajectories. The main
challenge of this problem is that the learner no longer has a simulator or``environment …

This paper introduces Honor of Kings Arena, a reinforcement learning (RL) environment
based on the Honor of Kings, one of the world's most popular games at present. Compared …

Video game development is getting increasingly effective as AI paradigms advance. Deep
Reinforcement Learning (DRL) is a promising artificial intelligence (AI) approach. It …

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) …