Search Results for author:
Found 12 papers, 6 papers with code
A Survey on Game Playing Agents and Large Models: Methods, Applications, and Challenges
The swift evolution of Large-scale Models (LMs), either language-focused or multi-modal, has garnered extensive attention in both academy and industry.
Towards General Computer Control: A Multimodal Agent for Red Dead Redemption II as a Case Study
Despite the success in specific tasks and scenarios, existing foundation agents, empowered by large models (LMs) and advanced tools, still cannot generalize to different scenarios, mainly due to dramatic differences in the observations and actions across scenarios.
Settling Decentralized Multi-Agent Coordinated Exploration by Novelty Sharing
The other is how agents can explore in a coordinated way.
Learning to Robustly Reconstruct Low-light Dynamic Scenes from Spike Streams
In this paper, we propose a bidirectional recurrent-based reconstruction framework, including a Light-Robust Representation (LR-Rep) and a fusion module, to better handle such extreme conditions.
CLIP4MC: An RL-Friendly Vision-Language Model for Minecraft
One of the essential missions in the AI research community is to build an autonomous embodied agent that can attain high-level performance across a wide spectrum of tasks.
Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning
We investigate the use of natural language to drive the generalization of policies in multi-agent settings.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Multi-Agent Sequential Decision-Making via Communication
Communication helps agents to obtain information about others so that better coordinated behavior can be learned.
Optical Flow Estimation for Spiking Camera
Further, for training SCFlow, we synthesize two sets of optical flow data for the spiking camera, SPIkingly Flying Things and Photo-realistic High-speed Motion, denoted as SPIFT and PHM respectively, corresponding to random high-speed and well-designed scenes.
Sequential Communication in Multi-Agent Reinforcement Learning
Agents determine the priority of decision-making by comparing the value of intention.
Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation
Recently, many deep learning methods have shown great success in providing promising solutions to many event-based problems, such as optical flow estimation.
Learning Individually Inferred Communication for Multi-Agent Cooperation
Empirically, we show that I2C can not only reduce communication overhead but also improve the performance in a variety of multi-agent cooperative scenarios, comparing to existing methods.
