17 citations · 54 across the 26 of their papers we have counts for
16 papers · 1 filter
Spectral Bellman Method: Unifying Representation and Exploration in RL
Ofir Nabati, Bo Dai, Shie Mannor +1
Representation learning is critical to the empirical and theoretical success of reinforcement learning. However, many existing methods are induced from model-learning aspects, misa…
Benchmarks for Reinforcement Learning with Biased Offline Data and Imperfect Simulators
Ori Linial, Guy Tennenholtz, Uri Shalit
In many reinforcement learning (RL) applications one cannot easily let the agent act in the world; this is true for autonomous vehicles, healthcare applications, and even some reco…
DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning
Anthony Liang, Guy Tennenholtz, Chih-wei Hsu +3
We introduce DynaMITE-RL, a meta-reinforcement learning (meta-RL) approach to approximate inference in environments where the latent state evolves at varying rates. We model episod…
Bayesian Regret Minimization in Offline Bandits
Marek Petrik, Guy Tennenholtz, Mohammad Ghavamzadeh
We study how to make decisions that minimize Bayesian regret in offline linear bandits. Prior work suggests that one must take actions with maximum lower confidence bound (LCB) on…
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding
Alizée Pace, Hugo Yèche, Bernhard Schölkopf +2
A prominent challenge of offline reinforcement learning (RL) is the issue of hidden confounding: unobserved variables may influence both the actions taken by the agent and the obse…
Representation-Driven Reinforcement Learning
Ofir Nabati, Guy Tennenholtz, Shie Mannor
We present a representation-driven framework for reinforcement learning. By representing policies as estimates of their expected values, we leverage techniques from contextual band…
Reinforcement Learning with History-Dependent Dynamic Contexts
Guy Tennenholtz, Nadav Merlis, Lior Shani +2
We introduce Dynamic Contextual Markov Decision Processes (DCMDPs), a novel reinforcement learning framework for history-dependent environments that generalizes the contextual MDP…
Reinforcement Learning with a Terminator
Guy Tennenholtz, Nadav Merlis, Lior Shani +5
We present the problem of reinforcement learning with exogenous termination. We define the Termination Markov Decision Process (TerMDP), an extension of the MDP framework, in which…
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…
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…
Uncertainty Estimation Using Riemannian Model Dynamics for Offline Reinforcement Learning
Guy Tennenholtz, Shie Mannor
Model-based offline reinforcement learning approaches generally rely on bounds of model error. Estimating these bounds is usually achieved through uncertainty estimation methods. I…
Bandits with Partially Observable Confounded Data
Guy Tennenholtz, Uri Shalit, Shie Mannor +1
We study linear contextual bandits with access to a large, confounded, offline dataset that was sampled from some fixed policy. We show that this problem is closely related to a va…
Never Worse, Mostly Better: Stable Policy Improvement in Deep Reinforcement Learning
Pranav Khanna, Guy Tennenholtz, Nadav Merlis +2
In recent years, there has been significant progress in applying deep reinforcement learning (RL) for solving challenging problems across a wide variety of domains. Nevertheless, c…
Off-Policy Evaluation in Partially Observable Environments
Guy Tennenholtz, Shie Mannor, Uri Shalit
This work studies the problem of batch off-policy evaluation for Reinforcement Learning in partially observable environments. Off-policy evaluation under partial observability is i…
Distributional Policy Optimization: An Alternative Approach for Continuous Control
Chen Tessler, Guy Tennenholtz, Shie Mannor
We identify a fundamental problem in policy gradient-based methods in continuous control. As policy gradient methods require the agent's underlying probability distribution, they l…