Reinforcement Learning
Appendix Effective Diversity in Population Based Reinforcement Learning 8 Additional Experiment Details
This result implies two things: 1.
Effective Diversity in Population Based Reinforcement Learning
Exploration is a key problem in reinforcement learning, since agents can only learn from data they acquire in the environment.
Multi-Agent Common Knowledge Reinforcement Learning
Christian Schroeder de Witt, Jakob Foerster, Gregory Farquhar, Philip Torr, Wendelin Boehmer, Shimon Whiteson
Neural Information Processing Systems http://nips.cc/
Unsupervised Learning of Object Keypoints for Perception and Control
Tejas D. Kulkarni, Ankush Gupta, Catalin Ionescu, Sebastian Borgeaud, Malcolm Reynolds, Andrew Zisserman, Volodymyr Mnih
To this end, we introduce Transporter, a neural network architecture for discovering concise geometric object representations in terms of keypoints or image-space coordinates.
A Appendix
Confined trust regions are a stable way of making large updates and avoiding pessimistic coefficients.
Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes
Our result is applicable in Reinforcement Learning (RL).
Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes
Our result is applicable in Reinforcement Learning (RL).