research
∙
06/04/2023
For SALE: State-Action Representation Learning for Deep Reinforcement Learning
In the field of reinforcement learning (RL), representation learning is ...
share
research
∙
05/19/2022
IL-flOw: Imitation Learning from Observation using Normalizing Flows
We present an algorithm for Inverse Reinforcement Learning (IRL) from ex...
research
∙
01/28/2022
Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error
In this work, we study the use of the Bellman equation as a surrogate ob...
research
∙
06/12/2021
A Minimalist Approach to Offline Reinforcement Learning
Offline reinforcement learning (RL) defines the task of learning from a ...
research
∙
06/12/2021
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation
Marginalized importance sampling (MIS), which measures the density ratio...
research
∙
07/12/2020
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay
Prioritized Experience Replay (PER) is a deep reinforcement learning tec...
research
∙
10/03/2019
Benchmarking Batch Deep Reinforcement Learning Algorithms
Widely-used deep reinforcement learning algorithms have been shown to fa...
research
∙
01/31/2019
GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects
Mesh models are a promising approach for encoding the structure of 3D ob...
research
∙
12/07/2018
Off-Policy Deep Reinforcement Learning without Exploration
Reinforcement learning traditionally considers the task of balancing exp...
research
∙
02/27/2018
3D Object Super-Resolution
We consider the problem of scaling deep generative shape models to high-...
research
∙
02/26/2018
