64 citations · 168 across the 26 of their papers we have counts for
4 papers · 1 filter
Bayesian Policy Gradients via Alpha Divergence Dropout Inference
Peter Henderson, Thang Doan, Riashat Islam +1
Policy gradient methods have had great success in solving continuous control tasks, yet the stochastic nature of such problems makes deterministic value estimation difficult. We pr…
OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning
Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon +3
Reinforcement learning has shown promise in learning policies that can solve complex problems. However, manually specifying a good reward function can be difficult, especially for…
Benchmark Environments for Multitask Learning in Continuous Domains
Peter Henderson, Wei-Di Chang, Florian Shkurti +3
As demand drives systems to generalize to various domains and problems, the study of multitask, transfer and lifelong learning has become an increasingly important pursuit. In disc…
Improved Adversarial Systems for 3D Object Generation and Reconstruction
Edward Smith, David Meger
This paper describes a new approach for training generative adversarial networks (GAN) to understand the detailed 3D shape of objects. While GANs have been used in this domain prev…