4.3k citations · 6.9k across the 51 of their papers we have counts for
8 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…
Domain Aggregation Networks for Multi-Source Domain Adaptation
Junfeng Wen, Russell Greiner, Dale Schuurmans
In many real-world applications, we want to exploit multiple source datasets of similar tasks to learn a model for a different but related target dataset -- e.g., recognizing chara…
An Optimistic Perspective on Offline Reinforcement Learning
Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi
Off-policy reinforcement learning (RL) using a fixed offline dataset of logged interactions is an important consideration in real world applications. This paper studies offline RL…
Advantage Amplification in Slowly Evolving Latent-State Environments
Martin Mladenov, Ofer Meshi, Jayden Ooi +2
Latent-state environments with long horizons, such as those faced by recommender systems, pose significant challenges for reinforcement learning (RL). In this work, we identify and…
Exponential Family Estimation via Adversarial Dynamics Embedding
Bo Dai, Zhen Liu, Hanjun Dai +4
We present an efficient algorithm for maximum likelihood estimation (MLE) of exponential family models, with a general parametrization of the energy function that includes neural n…
Learning to Generalize from Sparse and Underspecified Rewards
Rishabh Agarwal, Chen Liang, Dale Schuurmans +1
We consider the problem of learning from sparse and underspecified rewards, where an agent receives a complex input, such as a natural language instruction, and needs to generate a…