The synergistic combination of deep learning (DL) models and Earth observation (EO)
promises significant advances to support the Sustainable Development Goals (SDGs). New …

Observing and quantifying primate behavior in the wild is challenging. Human presence
affects primate behavior and habituation of new, especially terrestrial, individuals is a time …

Tackling real-world socio-economic challenges requires designing and testing economic
policies. However, this is hard in practice, due to a lack of appropriate (micro-level) …

In the empirical approach to game-theoretic analysis (EGTA), the model of the game comes
not from declarative representation, but is derived by interrogation of a procedural …

Multi-agent reinforcement learning (MARL) requires coordination to efficiently solve certain
tasks. Fully centralized control is often infeasible in such domains due to the size of joint …

Coordination is one of the essential problems in multi-agent systems. Typically multi-agent
reinforcement learning (MARL) methods treat agents equally and the goal is to solve the …

Incentive mechanisms are central to the functionality of permissionless blockchains: they
incentivize participants to run and secure the underlying consensus protocol. Designing …

Game theory provides a mathematical way to study the interaction between multiple
decision makers. However, classical game-theoretic analysis is limited in scalability due to …

We propose a simple, general and effective technique, Reward Randomization for
discovering diverse strategic policies in complex multi-agent games. Combining reward …

Recent years have witnessed great potential in applying Deep Reinforcement Learning
(DRL) in various challenging applications, such as autonomous driving, nuclear fusion …