163 citations · 190 across the 3 of their papers we have counts for
3 papers
cs.LG2021
Maximum Entropy Reinforcement Learning with Mixture Policies
Nir Baram, Guy Tennenholtz, Shie Mannor
Mixture models are an expressive hypothesis class that can approximate a rich set of policies. However, using mixture policies in the Maximum Entropy (MaxEnt) framework is not stra…
stat.ML2016★ 27 cited
Model-based Adversarial Imitation Learning
Nir Baram, Oron Anschel, Shie Mannor
Generative adversarial learning is a popular new approach to training generative models which has been proven successful for other related problems as well. The general idea is to…
cs.AI2016★ 163 cited
Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning
Oron Anschel, Nir Baram, Nahum Shimkin
Instability and variability of Deep Reinforcement Learning (DRL) algorithms tend to adversely affect their performance. Averaged-DQN is a simple extension to the DQN algorithm, bas…