4 citations · 8 across the 5 of their papers we have counts for
9 papers
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning
Guy Tennenholtz, Assaf Hallak, Gal Dalal +3
We consider the problem of using expert data with unobserved confounders for imitation and reinforcement learning. We begin by defining the problem of learning from confounded expe…
Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning
Roy Zohar, Shie Mannor, Guy Tennenholtz
Cooperative multi-agent reinforcement learning (MARL) faces significant scalability issues due to state and action spaces that are exponentially large in the number of agents. As e…
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…
The Pendulum Arrangement: Maximizing the Escape Time of Heterogeneous Random Walks
Asaf Cassel, Shie Mannor, Guy Tennenholtz
We identify a fundamental phenomenon of heterogeneous one dimensional random walks: the escape (traversal) time is maximized when the heterogeneity in transition probabilities form…
Language is Power: Representing States Using Natural Language in Reinforcement Learning
Erez Schwartz, Guy Tennenholtz, Chen Tessler +1
Recent advances in reinforcement learning have shown its potential to tackle complex real-life tasks. However, as the dimensionality of the task increases, reinforcement learning m…