Satisficing paths and independent multiagent reinforcement learning in stochastic games
B Yongacoglu, G Arslan, S Yüksel - SIAM Journal on Mathematics of Data …, 2023 - SIAM
In multiagent reinforcement learning, independent learners are those that do not observe the
actions of other agents in the system. Due to the decentralization of information, it is …
actions of other agents in the system. Due to the decentralization of information, it is …
Information-guided temporal logic inference with prior knowledge
This paper investigates the problem of inferring knowledge from data that is interpretable
and informative to humans who have prior knowledge. Specifically, given a dataset as a …
and informative to humans who have prior knowledge. Specifically, given a dataset as a …
Learning deception using fuzzy multi-level reinforcement learning in a multi-defender one-invader differential game
Differential games are a class of game theory problems governed by differential equations.
Differential games are often defined in the continuous domain and solved by the calculus of …
Differential games are often defined in the continuous domain and solved by the calculus of …
[PDF][PDF] Learning multi-objective deception in a two-player differential game using reinforcement learning and multi-objective genetic algorithm
In this paper, a framework is established to model a deceitful agent and train it in an
adversarial two-player game. In the game, a player uses multi-objective deception to …
adversarial two-player game. In the game, a player uses multi-objective deception to …
Deceptive reinforcement learning for privacy-preserving planning
In this paper, we study the problem of deceptive reinforcement learning to preserve the
privacy of a reward function. Reinforcement learning is the problem of finding a behaviour …
privacy of a reward function. Reinforcement learning is the problem of finding a behaviour …
Deceptive decision-making under uncertainty
We study the design of autonomous agents that are capable of deceiving outside observers
about their intentions while carrying out tasks in stochastic, complex environments. By …
about their intentions while carrying out tasks in stochastic, complex environments. By …
Deception in supervisory control
The use of deceptive strategies is important for an agent that attempts not to reveal his
intentions in an adversarial environment. We consider a setting, in which a supervisor …
intentions in an adversarial environment. We consider a setting, in which a supervisor …
Deceptive reinforcement learning in model-free domains
A Lewis, T Miller - Proceedings of the International Conference on …, 2023 - ojs.aaai.org
This paper investigates deceptive reinforcement learning for privacy preservation in model-
free and continuous action space domains. In reinforcement learning, the reward function …
free and continuous action space domains. In reinforcement learning, the reward function …
Deceptive planning for resource allocation
We consider a team of autonomous agents that navigate in an adversarial environment and
aim to achieve a task by allocating their resources over a set of target locations. An …
aim to achieve a task by allocating their resources over a set of target locations. An …
Deception by Motion: The Eater and the Mover Game
V Rostobaya, Y Guan, J Berneburg… - IEEE Control …, 2023 - ieeexplore.ieee.org
We study the idea of “deception by motion” through a two-player dynamic game played
between a Mover who must reach its goal to retrieve resources, and an Eater who can …
between a Mover who must reach its goal to retrieve resources, and an Eater who can …