163 citations · 194 across the 6 of their papers we have counts for
6 papers
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…
Action Redundancy in Reinforcement Learning
Nir Baram, Guy Tennenholtz, Shie Mannor
Maximum Entropy (MaxEnt) reinforcement learning is a powerful learning paradigm which seeks to maximize return under entropy regularization. However, action entropy does not necess…
Inspiration Learning through Preferences
Nir Baram, Shie Mannor
Current imitation learning techniques are too restrictive because they require the agent and expert to share the same action space. However, oftentimes agents that act differently…
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…
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…
Deep Reinforcement Learning Discovers Internal Models
Nir Baram, Tom Zahavy, Shie Mannor
Deep Reinforcement Learning (DRL) is a trending field of research, showing great promise in challenging problems such as playing Atari, solving Go and controlling robots. While DRL…