receiver
- North America > United States > California (0.04)
- Asia > Middle East > Israel > Jerusalem District > Jerusalem (0.04)
- Research Report > Experimental Study (1.00)
- Research Report > New Finding (0.68)
- North America > United States (0.46)
- Europe > Italy > Lazio > Rome (0.04)
- Europe > Germany > Berlin (0.04)
- Europe > Kosovo > District of Gjilan > Kamenica (0.04)
- Asia > Middle East > Jordan (0.04)
- Information Technology > Game Theory (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (0.46)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.46)
- Information Technology > Data Science > Data Mining > Big Data (0.45)
- Information Technology > Sensing and Signal Processing > Image Processing (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Agents (1.00)
- Information Technology > Artificial Intelligence > Natural Language (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.93)
Information Design in Multi-Agent Reinforcement Learning
To thrive in those environments, the agent needs to influence other agents so their actions become more helpful and less harmful. Research in computational economics distills two ways to influence others directly: by providing tangible goods ( mechanism design) and by providing information ( information design). This work investigates information design problems for a group of RL agents. The main challenges are two-fold. One is the information provided will immediately affect the transition of the agent trajectories, which introduces additional non-stationarity. The other is the information can be ignored, so the sender must provide information that the receiver is willing to respect.
- Asia > China > Guangdong Province > Shenzhen (0.04)
- Europe > Kosovo > District of Gjilan > Kamenica (0.04)
- Asia > China > Hong Kong (0.04)
- (4 more...)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Agents (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Undirected Networks > Markov Models (0.67)
Information Design in Multi-Agent Reinforcement Learning
To thrive in those environments, the agent needs to influence other agents so their actions become more helpful and less harmful. Research in computational economics distills two ways to influence others directly: by providing tangible goods ( mechanism design) and by providing information ( information design). This work investigates information design problems for a group of RL agents. The main challenges are two-fold. One is the information provided will immediately affect the transition of the agent trajectories, which introduces additional non-stationarity. The other is the information can be ignored, so the sender must provide information that the receiver is willing to respect.
- Asia > China > Guangdong Province > Shenzhen (0.04)
- Europe > Kosovo > District of Gjilan > Kamenica (0.04)
- Asia > China > Hong Kong (0.04)
- (4 more...)
- North America > United States > Massachusetts > Middlesex County > Cambridge (0.14)
- North America > United States > California > Los Angeles County > Long Beach (0.04)
- North America > United States > Florida > Miami-Dade County > Miami Beach (0.04)
- (8 more...)
- Europe > Kosovo > District of Gjilan > Kamenica (0.05)
- North America > United States (0.04)
- North America > Canada (0.04)
- (2 more...)
- Europe > Kosovo > District of Gjilan > Kamenica (0.04)
- Asia > Middle East > Jordan (0.04)