187 citations · 253 across the 14 of their papers we have counts for
4 papers · 2 filters
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…
Deep Reinforcement Learning that Matters
Peter Henderson, Riashat Islam, Philip Bachman +3
In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and acc…
Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
Riashat Islam, Peter Henderson, Maziar Gomrokchi +1
Policy gradient methods in reinforcement learning have become increasingly prevalent for state-of-the-art performance in continuous control tasks. Novel methods typically benchmark…