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Found 17 papers, 5 papers with code
Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective
Graphs are a natural representation for systems based on relations between connected entities.
Dynamics of Moral Behavior in Heterogeneous Populations of Learning Agents
In multi-agent (social) environments, complex population-level phenomena may emerge from interactions between individual learning agents.
Information-Theoretic State Variable Selection for Reinforcement Learning
Identifying the most suitable variables to represent the state is a fundamental challenge in Reinforcement Learning (RL).
Learning Machine Morality through Experience and Interaction
In particular, we present three case studies of recent works which use learning from experience (i. e., Reinforcement Learning) to explicitly provide moral principles to learning agents - either as intrinsic rewards, moral logical constraints or textual principles for language models.
Tree Search in DAG Space with Model-based Reinforcement Learning for Causal Discovery
Identifying causal structure is central to many fields ranging from strategic decision-making to biology and economics.
Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement Learning
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.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Graph Neural Modeling of Network Flows
Network flow problems, which involve distributing traffic such that the underlying infrastructure is used effectively, are ubiquitous in transportation and logistics.
Dynamic Network Reconfiguration for Entropy Maximization using Deep Reinforcement Learning
A key problem in network theory is how to reconfigure a graph in order to optimize a quantifiable objective.
Trust-based Consensus in Multi-Agent Reinforcement Learning Systems
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.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Solving Graph-based Public Goods Games with Tree Search and Imitation Learning
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.
Solving Graph-based Public Good Games with Tree Search and Imitation Learning
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.
Planning Spatial Networks with Monte Carlo Tree Search
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).
Cooperation and Reputation Dynamics with Reinforcement Learning
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.
Identifying Vulnerabilities of Industrial Control Systems using Evolutionary Multiobjective Optimisation
In this paper we propose a novel methodology to assist in identifying vulnerabilities in a real-world complex heterogeneous industrial control systems (ICS) using two evolutionary multiobjective optimisation (EMO) algorithms, NSGA-II and SPEA2.
Goal-directed graph construction using reinforcement learning
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.
Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning
Social dilemmas have been widely studied to explain how humans are able to cooperate in society.
Online Collective Animal Movement Activity Recognition
Learning the activities of animals is important for the purpose of monitoring their welfare vis a vis their behaviour with respect to their environment and conspecifics.
