154 citations · 559 across the 26 of their papers we have counts for
5 papers · 1 filter
AlgaeDICE: Policy Gradient from Arbitrary Experience
Ofir Nachum, Bo Dai, Ilya Kostrikov +3
In many real-world applications of reinforcement learning (RL), interactions with the environment are limited due to cost or feasibility. This presents a challenge to traditional R…
CAQL: Continuous Action Q-Learning
Moonkyung Ryu, Yinlam Chow, Ross Anderson +2
Value-based reinforcement learning (RL) methods like Q-learning have shown success in a variety of domains. One challenge in applying Q-learning to continuous-action RL problems, h…
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control
Nir Levine, Yinlam Chow, Rui Shu +3
Many real-world sequential decision-making problems can be formulated as optimal control with high-dimensional observations and unknown dynamics. A promising approach is to embed t…
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections
Ofir Nachum, Yinlam Chow, Bo Dai +1
In many real-world reinforcement learning applications, access to the environment is limited to a fixed dataset, instead of direct (online) interaction with the environment. When u…
Lyapunov-based Safe Policy Optimization for Continuous Control
Yinlam Chow, Ofir Nachum, Aleksandra Faust +2
We study continuous action reinforcement learning problems in which it is crucial that the agent interacts with the environment only through safe policies, i.e.,~policies that do n…