5 papers · 1 filter
Bayesian Meta-Reinforcement Learning with Laplace Variational Recurrent Networks
Joery A. de Vries, Jinke He, Mathijs M. de Weerdt +1
Meta-reinforcement learning trains a single reinforcement learning agent on a distribution of tasks to quickly generalize to new tasks outside of the training set at test time. Fro…
Trust-Region Twisted Policy Improvement
Joery A. de Vries, Jinke He, Yaniv Oren +1
Monte-Carlo tree search (MCTS) has driven many recent breakthroughs in deep reinforcement learning (RL). However, scaling MCTS to parallel compute has proven challenging in practic…
RangL: A Reinforcement Learning Competition Platform
Viktor Zobernig, Richard A. Saldanha, Jinke He +12
The RangL project hosted by The Alan Turing Institute aims to encourage the wider uptake of reinforcement learning by supporting competitions relating to real-world dynamic decisio…
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…