-
Biases for Emergent Communication in Multi-agent Reinforcement Learning
We study the problem of emergent communication, in which language arises...
12/11/2019 ∙ by Tom Eccles, et al. ∙
0
∙
share
read it
-
A Generalized Training Approach for Multiagent Learning
This paper investigates a population-based training regime based on game...
09/27/2019 ∙ by Paul Müller, et al. ∙
20
∙
share
read it
-
Emergent Coordination Through Competition
We study the emergence of cooperative behaviors in reinforcement learnin...
02/19/2019 ∙ by Siqi Liu, et al. ∙
2
∙
share
read it
-
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning
Recent progress in artificial intelligence through reinforcement learnin...
07/03/2018 ∙ by Max Jaderberg, et al. ∙
2
∙
share
read it
-
Value-Decomposition Networks For Cooperative Multi-Agent Learning
We study the problem of cooperative multi-agent reinforcement learning w...
06/16/2017 ∙ by Peter Sunehag, et al. ∙
0
∙
share
read it
-
Nesterov's Accelerated Gradient and Momentum as approximations to Regularised Update Descent
We present a unifying framework for adapting the update direction in gra...
07/07/2016 ∙ by Aleksandar Botev, et al. ∙
0
∙
share
read it
-
A Gauss-Newton Method for Markov Decision Processes
Approximate Newton methods are a standard optimization tool which aim to...
07/29/2015 ∙ by Thomas Furmston, et al. ∙
0
∙
share
read it
-
Conditional mean embeddings as regressors - supplementary
We demonstrate an equivalence between reproducing kernel Hilbert space (...
05/21/2012 ∙ by Steffen Grunewalder, et al. ∙
0
∙
share
read it
