643 citations · 2.1k across the 8 of their papers we have counts for
5 papers · 1 filter
Learning Latent Dynamics for Planning from Pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer +4
Planning has been very successful for control tasks with known environment dynamics. To leverage planning in unknown environments, the agent needs to learn the dynamics from intera…
Reinforcement Learning for Improving Agent Design
David Ha
In many reinforcement learning tasks, the goal is to learn a policy to manipulate an agent, whose design is fixed, to maximize some notion of cumulative reward. The design of the a…
Recurrent World Models Facilitate Policy Evolution
David Ha, Jürgen Schmidhuber
A generative recurrent neural network is quickly trained in an unsupervised manner to model popular reinforcement learning environments through compressed spatio-temporal represent…
Pommerman: A Multi-Agent Playground
Cinjon Resnick, Wes Eldridge, David Ha +5
We present Pommerman, a multi-agent environment based on the classic console game Bomberman. Pommerman consists of a set of scenarios, each having at least four players and contain…
World Models
David Ha, Jürgen Schmidhuber
We explore building generative neural network models of popular reinforcement learning environments. Our world model can be trained quickly in an unsupervised manner to learn a com…