Search Results for author:
Found 7 papers, 4 papers with code
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control
They also allow practitioners to inject biases encoded in the structure of the input graph.
Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning
Non-stationarity can arise in Reinforcement Learning (RL) even in stationary environments.
Privileged Information Dropout in Reinforcement Learning
Using privileged information during training can improve the sample efficiency and performance of machine learning systems.
Multi-agent Hierarchical Reinforcement Learning with Dynamic Termination
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
Deep Residual Reinforcement Learning
We revisit residual algorithms in both model-free and model-based reinforcement learning settings.
Model-based Reinforcement Learning
reinforcement-learning
+1
Generalized Off-Policy Actor-Critic
We propose a new objective, the counterfactual objective, unifying existing objectives for off-policy policy gradient algorithms in the continuing reinforcement learning (RL) setting.
Multi-Agent Common Knowledge Reinforcement Learning
In this paper, we show that common knowledge between agents allows for complex decentralised coordination.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
