66 citations · 67 across the 3 of their papers we have counts for
6 papers · 1 filter
Ensemble Bootstrapping for Q-Learning
Oren Peer, Chen Tessler, Nadav Merlis +1
Q-learning (QL), a common reinforcement learning algorithm, suffers from over-estimation bias due to the maximization term in the optimal Bellman operator. This bias may lead to su…
Reward Tweaking: Maximizing the Total Reward While Planning for Short Horizons
Chen Tessler, Shie Mannor
In reinforcement learning, the discount factor controls the agent's effective planning horizon. Traditionally, this parameter was considered part of the MDP; however, as deep r…
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…
Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces
Chen Tessler, Tom Zahavy, Deborah Cohen +2
We propose a computationally efficient algorithm that combines compressed sensing with imitation learning to solve text-based games with combinatorial action spaces. Specifically,…
Action Robust Reinforcement Learning and Applications in Continuous Control
Chen Tessler, Yonathan Efroni, Shie Mannor
A policy is said to be robust if it maximizes the reward while considering a bad, or even adversarial, model. In this work we formalize two new criteria of robustness to action unc…
Reward Constrained Policy Optimization
Chen Tessler, Daniel J. Mankowitz, Shie Mannor
Solving tasks in Reinforcement Learning is no easy feat. As the goal of the agent is to maximize the accumulated reward, it often learns to exploit loopholes and misspecifications…