activity
20182026
most citedA Survey of Deep Reinforcement Learning in Video Games

149 citations · 214 across the 18 of their papers we have counts for

collaborators

22 papers

cs.LG2026

Revisiting On-Policy Distillation: Empirical Failure Modes and Simple Fixes

Yuqian Fu, Haohuan Huang, Kaiwen Jiang +4

On-policy distillation (OPD) is increasingly used in LLM post-training because it can leverage a teacher model to provide dense supervision on student rollouts. The standard implem…

cs.LG2025

Equilibrium Policy Generalization: A Reinforcement Learning Framework for Cross-Graph Zero-Shot Generalization in Pursuit-Evasion Games

Runyu Lu, Peng Zhang, Ruochuan Shi +5

Equilibrium learning in adversarial games is an important topic widely examined in the fields of game theory and reinforcement learning (RL). Pursuit-evasion game (PEG), as an impo…

cs.CL2025

CriticSearch: Fine-Grained Credit Assignment for Search Agents via a Retrospective Critic

Yaocheng Zhang, Haohuan Huang, Zijun Song +4

Tool-Integrated Reasoning (TIR) with search engines enables large language models to iteratively retrieve up-to-date external knowledge, enhancing adaptability and generalization i…

cs.LG2025

ARAC: Adaptive Regularized Multi-Agent Soft Actor-Critic in Graph-Structured Adversarial Games

Ruochuan Shi, Runyu Lu, Yuanheng Zhu +1

In graph-structured multi-agent reinforcement learning (MARL) adversarial tasks such as pursuit and confrontation, agents must coordinate under highly dynamic interactions, where s…

cs.LG2025

R2PS: Worst-Case Robust Real-Time Pursuit Strategies under Partial Observability

Runyu Lu, Ruochuan Shi, Yuanheng Zhu +1

Computing worst-case robust strategies in pursuit-evasion games (PEGs) is time-consuming, especially when real-world factors like partial observability are considered. While import…

cs.RO2025

Empowering Multi-Robot Cooperation via Sequential World Models

Zijie Zhao, Honglei Guo, Shengqian Chen +4

Model-based reinforcement learning (MBRL) has achieved remarkable success in robotics due to its high sample efficiency and planning capability. However, extending MBRL to physical…