Search Results for author:
Found 11 papers, 6 papers with code
CuDA2: An approach for Incorporating Traitor Agents into Cooperative Multi-Agent Systems
Previous works on adversarial attacks have primarily focused on white-box attacks that directly perturb the states or actions of victim agents, often in scenarios with a limited number of attacks.
Mini Honor of Kings: A Lightweight Environment for Multi-Agent Reinforcement Learning
To address these issues, we introduce the first publicly available map editor for the popular mobile game Honor of Kings and design a lightweight environment, Mini Honor of Kings (Mini HoK), for researchers to conduct experiments.
ALISA: Accelerating Large Language Model Inference via Sparsity-Aware KV Caching
In a single GPU-CPU system, we demonstrate that under varying workloads, ALISA improves the throughput of baseline systems such as FlexGen and vLLM by up to 3X and 1. 9X, respectively.
Merino: Entropy-driven Design for Generative Language Models on IoT Devices
Generative Large Language Models (LLMs) stand as a revolutionary advancement in the modern era of artificial intelligence (AI).
DanZero+: Dominating the GuanDan Game through Reinforcement Learning
The utilization of artificial intelligence (AI) in card games has been a well-explored subject within AI research for an extensive period.
DanZero: Mastering GuanDan Game with Reinforcement Learning
We compare it with 8 baseline AI programs which are based on heuristic rules and the results reveal the outstanding performance of DanZero.
Lightweight Vision Transformer with Cross Feature Attention
Recent advances in vision transformers (ViTs) have achieved great performance in visual recognition tasks.
Semi-Supervised Segmentation of Mitochondria from Electron Microscopy Images Using Spatial Continuity
Fully supervised deep learning models developed for this task achieve excellent performance but require substantial amounts of annotated data for training.
DouZero+: Improving DouDizhu AI by Opponent Modeling and Coach-guided Learning
Recent years have witnessed the great breakthrough of deep reinforcement learning (DRL) in various perfect and imperfect information games.
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agents
To the best of our knowledge, this work is the first to study the unexpected crashes in the multi-agent system.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement Learning
Specifically, we model both individual Q-values and global Q-value with categorical distribution.
