Centralised training with decentralised execution is an important setting for cooperative
deep multi-agent reinforcement learning due to communication constraints during execution …

Role-based learning holds the promise of achieving scalable multi-agent learning by
decomposing complex tasks using roles. However, it is largely unclear how to efficiently …

This paper introduces MDP homomorphic networks for deep reinforcement learning. MDP
homomorphic networks are neural networks that are equivariant under symmetries in the …

VDN and QMIX are two popular value-based algorithms for cooperative MARL that learn a
centralized action value function as a monotonic mixing of per-agent utilities. While this …

Abstraction has been widely studied as a way to improve the efficiency and generalization of
reinforcement learning algorithms. In this paper, we study abstraction in the continuous …

Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …

Reinforcement Learning in large action spaces is a challenging problem. This is especially
true for cooperative multi-agent reinforcement learning (MARL), which often requires …

Applying probabilistic models to reinforcement learning (RL) enables the uses of powerful
optimisation tools such as variational inference in RL. However, existing inference …

Bimanual manipulation is important for building intelligent robots that unlock richer skills
than single arms. We consider a multi-object bimanual rearrangement task, where a …

Deep Reinforcement Learning (RL) is a promising approach for adaptive robot control, but
its current application to robotics is currently hindered by high sample requirements. To …