1 code implementation • ICLR 2021 • Vitaly Kurin, Maximilian Igl, Tim Rocktäschel, Wendelin Boehmer, Shimon Whiteson
They also allow practitioners to inject biases encoded in the structure of the input graph.
no code implementations • ICLR 2021 • Maximilian Igl, Gregory Farquhar, Jelena Luketina, Wendelin Boehmer, Shimon Whiteson
Non-stationarity can arise in Reinforcement Learning (RL) even in stationary environments.
no code implementations • 19 May 2020 • Pierre-Alexandre Kamienny, Kai Arulkumaran, Feryal Behbahani, Wendelin Boehmer, Shimon Whiteson
Using privileged information during training can improve the sample efficiency and performance of machine learning systems.
no code implementations • 21 Oct 2019 • Dongge Han, Wendelin Boehmer, Michael Wooldridge, Alex Rogers
We evaluate our model empirically on a set of multi-agent pursuit and taxi tasks, and show that our agents learn to adapt flexibly across scenarios that require different termination behaviours.
Hierarchical Reinforcement Learning reinforcement-learning +1
1 code implementation • 3 May 2019 • Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson
We revisit residual algorithms in both model-free and model-based reinforcement learning settings.
Model-based Reinforcement Learning reinforcement-learning +1
1 code implementation • NeurIPS 2019 • Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson
We propose a new objective, the counterfactual objective, unifying existing objectives for off-policy policy gradient algorithms in the continuing reinforcement learning (RL) setting.
1 code implementation • NeurIPS 2019 • Christian A. Schroeder de Witt, Jakob N. Foerster, Gregory Farquhar, Philip H. S. Torr, Wendelin Boehmer, Shimon Whiteson
In this paper, we show that common knowledge between agents allows for complex decentralised coordination.
Multi-agent Reinforcement Learning reinforcement-learning +3