6 papers
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…
Benchmarking Robustness and Generalization in Multi-Agent Systems: A Case Study on Neural MMO
Yangkun Chen, Joseph Suarez, Junjie Zhang +18
We present the results of the second Neural MMO challenge, hosted at IJCAI 2022, which received 1600+ submissions. This competition targets robustness and generalization in multi-a…
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…