In particular, we believe that an interesting and insightful starting point is the analysis of emergent behavior of Reinforcement Learning (RL) agents that act according to a predefined set of moral rewards in social dilemmas.
Direct punishment is an ubiquitous social mechanism that has been shown to benefit the emergence of cooperation within the natural world, however no prior work has investigated its impact on populations of learning agents.
One of the major challenges in Deep Reinforcement Learning for control is the need for extensive training to learn the policy.
The widely-used objective that we focus on is the maximum utilization of any link in the network, given traffic demands and a routing strategy.
A key problem in network theory is how to reconfigure a graph in order to optimize a quantifiable objective.
An often neglected issue in multi-agent reinforcement learning (MARL) is the potential presence of unreliable agents in the environment whose deviations from expected behavior can prevent a system from accomplishing its intended tasks.
In particular, we define a Markov Decision Process which incrementally generates an mIS, and adopt a planning method to search for equilibria, outperforming existing methods.
The growing number of applications of Reinforcement Learning (RL) in real-world domains has led to the development of privacy-preserving techniques due to the inherently sensitive nature of data.
Public goods games represent insightful settings for studying incentives for individual agents to make contributions that, while costly for each of them, benefit the wider society.
We tackle the problem of goal-directed graph construction: given a starting graph, a budget of modifications, and a global objective function, the aim is to find a set of edges whose addition to the graph achieves the maximum improvement in the objective (e. g., communication efficiency).
In this article, we consider a set of key questions in the area of generative deep learning for the arts, including the following: is it possible to use copyrighted works as training set for generative models?
We use a simple model of reinforcement learning to show that reputation mechanisms generate two coordination problems: agents need to learn how to coordinate on the meaning of existing reputations and collectively agree on a social norm to assign reputations to others based on their behavior.
Our approach is an alternative to standard label-switching techniques and provides a single posterior summary set of topics, as well as associated measures of uncertainty.
In this work, we formulate the construction of a graph as a decision-making process in which a central agent creates topologies by trial and error and receives rewards proportional to the value of the target objective.
In this paper, we design and evaluate six different graph input representations (i. e., representations of the network paths), by considering the network's topological and temporal characteristics, for being used as inputs for machine learning models to learn the behavior of urban networks paths.
Social dilemmas have been widely studied to explain how humans are able to cooperate in society.
Using social dilemmas as the training ground, we present a novel learning architecture, Learning through Probing (LTP), where agents utilize a probing mechanism to incorporate how their opponent's behavior changes when an agent takes an action.
Metadata are associated to most of the information we produce in our daily interactions and communication in the digital world.
We characterize the mobility patterns of individuals using the GPS metrics presented in the literature and employ these metrics as input to the network.
Our everyday interactions with pervasive systems generate traces that capture various aspects of human behavior and enable machine learning algorithms to extract latent information about users.
The abundance of data produced daily from large variety of sources has boosted the need of novel approaches on causal inference analysis from observational data.
In this paper, we present a large-scale study of recipes published on the web and their content, aiming to understand cuisines and culinary habits around the world.