3 citations · 7 across the 8 of their papers we have counts for
5 papers · 1 filter
Breaking Habits: On the Role of the Advantage Function in Learning Causal State Representations
Miguel Suau
Recent work has shown that reinforcement learning agents can develop policies that exploit spurious correlations between rewards and observations. This phenomenon, known as policy…
Bad Habits: Policy Confounding and Out-of-Trajectory Generalization in RL
Miguel Suau, Matthijs T. J. Spaan, Frans A. Oliehoek
Reinforcement learning agents tend to develop habits that are effective only under specific policies. Following an initial exploration phase where agents try out different actions,…
Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems
Miguel Suau, Jinke He, Mustafa Mert Çelikok +2
Due to its high sample complexity, simulation is, as of today, critical for the successful application of reinforcement learning. Many real-world problems, however, exhibit overly…
Influence-Augmented Local Simulators: A Scalable Solution for Fast Deep RL in Large Networked Systems
Miguel Suau, Jinke He, Matthijs T. J. Spaan +1
Learning effective policies for real-world problems is still an open challenge for the field of reinforcement learning (RL). The main limitation being the amount of data needed and…
Influence-aware Memory Architectures for Deep Reinforcement Learning
Miguel Suau, Jinke He, Elena Congeduti +3
Due to its perceptual limitations, an agent may have too little information about the state of the environment to act optimally. In such cases, it is important to keep track of the…