125 citations · 148 across the 19 of their papers we have counts for
3 papers · 1 filter
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…
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies
Yonathan Efroni, Nadav Merlis, Mohammad Ghavamzadeh +1
State-of-the-art efficient model-based Reinforcement Learning (RL) algorithms typically act by iteratively solving empirical models, i.e., by performing \emph{full-planning} on Mar…
Batch-Size Independent Regret Bounds for the Combinatorial Multi-Armed Bandit Problem
Nadav Merlis, Shie Mannor
We consider the combinatorial multi-armed bandit (CMAB) problem, where the reward function is nonlinear. In this setting, the agent chooses a batch of arms on each round and receiv…