research
∙
09/22/2022
An Investigation of the Bias-Variance Tradeoff in Meta-Gradients
Meta-gradients provide a general approach for optimizing the meta-parame...
share
research
∙
10/25/2021
Self-Consistent Models and Values
Learned models of the environment provide reinforcement learning (RL) ag...
research
∙
02/24/2021
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning
We study reinforcement learning (RL) with no-reward demonstrations, a se...
research
∙
06/18/2020
Weighted QMIX: Expanding Monotonic Value Function Factorisation
QMIX is a popular Q-learning algorithm for cooperative MARL in the centr...
research
∙
06/10/2020
The Impact of Non-stationarity on Generalisation in Deep Reinforcement Learning
Non-stationarity arises in Reinforcement Learning (RL) even in stationar...
research
∙
03/19/2020
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
In many real-world settings, a team of agents must coordinate its behavi...
research
∙
09/23/2019
Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Estimators for Reinforcement Learning
Gradient-based methods for optimisation of objectives in stochastic sett...
research
∙
06/28/2019
Growing Action Spaces
In complex tasks, such as those with large combinatorial action spaces, ...
research
∙
06/10/2019
A Survey of Reinforcement Learning Informed by Natural Language
To be successful in real-world tasks, Reinforcement Learning (RL) needs ...
research
∙
02/11/2019
The StarCraft Multi-Agent Challenge
In the last few years, deep multi-agent reinforcement learning (RL) has ...
research
∙
10/27/2018
Multi-Agent Common Knowledge Reinforcement Learning
In multi-agent reinforcement learning, centralised policies can only be ...
research
∙
03/30/2018
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
In many real-world settings, a team of agents must coordinate their beha...
research
∙
10/31/2017
TreeQN and ATreeC: Differentiable Tree Planning for Deep Reinforcement Learning
Combining deep model-free reinforcement learning with on-line planning i...
research
∙
05/24/2017
Counterfactual Multi-Agent Policy Gradients
Cooperative multi-agent systems can be naturally used to model many real...
research
∙
02/28/2017
