Markov Models
On the Role of Information Structure in Reinforcement Learning for Partially-Observable Sequential Teams and Games
In this paper, we formalize a novel reinforcement learning model which explicitly represents the information structure.
Universally Expressive Communication in Multi-Agent Reinforcement Learning
By observing that many existing protocols can be viewed as instances of graph neural networks (GNNs), we demonstrate the equivalence of joint action selection to node labelling.
On the Stochastic Stability of Deep Markov Models
This section proposes additional regularization methods for learning stable deep Markov models. The most direct approach is to include the stability conditions as extra penalties in the DMM loss function.
On the Stochastic Stability of Deep Markov Models
In this paper, we provide sufficient conditions of DMM's stochastic stability as defined in the context of dynamical systems
