Influence-Augmented Local Simulators: a Scalable Solution for Fast Deep RL in Large Networked Systems

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 the pace at which that data can be obtained. In this paper, we study how to build lightweight simulators of complicated systems that can run sufficiently fast for...

Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems

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 complex dynamics, which makes their full-scale simulation computationally slow. In this paper, we show how to factorize large networked systems of many agents into...

Speeding up Deep Reinforcement Learning through Influence-Augmented Local Simulators

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 the pace at which that data can be obtained. In this paper, we study how to build lightweight simulators of complicated systems that can run sufficiently fast for...