9 citations · 19 across the 9 of their papers we have counts for
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cs.AI2020
Online Learning of Non-Markovian Reward Models
Gavin Rens, Jean-François Raskin, Raphaël Reynouad +1
There are situations in which an agent should receive rewards only after having accomplished a series of previous tasks, that is, rewards are non-Markovian. One natural and quite g…
cs.AI2020
Reputation-driven Decision-making in Networks of Stochastic Agents
David Maoujoud, Gavin Rens
This paper studies multi-agent systems that involve networks of self-interested agents. We propose a Markov Decision Process-derived framework, called RepNet-MDP, tailored to domai…
cs.AI2020★ 9 cited
Learning Non-Markovian Reward Models in MDPs
Gavin Rens, Jean-François Raskin
There are situations in which an agent should receive rewards only after having accomplished a series of previous tasks. In other words, the reward that the agent receives is non-M…