254 citations · 362 across the 10 of their papers we have counts for
5 papers · 1 filter
Universal Successor Features Approximators
Diana Borsa, André Barreto, John Quan +5
The ability of a reinforcement learning (RL) agent to learn about many reward functions at the same time has many potential benefits, such as the decomposition of complex tasks int…
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning
Tom Zahavy, Matan Haroush, Nadav Merlis +2
Learning how to act when there are many available actions in each state is a challenging task for Reinforcement Learning (RL) agents, especially when many of the actions are redund…
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…
Soft-Robust Actor-Critic Policy-Gradient
Esther Derman, Daniel J. Mankowitz, Timothy A. Mann +1
Robust Reinforcement Learning aims to derive optimal behavior that accounts for model uncertainty in dynamical systems. However, previous studies have shown that by considering the…
Learning Robust Options
Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon +2
Robust reinforcement learning aims to produce policies that have strong guarantees even in the face of environments/transition models whose parameters have strong uncertainty. Exis…