Search Results for author:
Found 17 papers, 14 papers with code
Active Offline Policy Selection
We use multiple benchmarks, including real-world robotics, with a large number of candidate policies to show that the proposed approach improves upon state-of-the-art OPE estimates and pure online policy evaluation.
On Instrumental Variable Regression for Deep Offline Policy Evaluation
By applying different IV techniques to OPE, we are not only able to recover previously proposed OPE methods such as model-based techniques but also to obtain competitive new techniques.
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization
This modeling choice assumes that different dimensions of the next state and reward are conditionally independent given the current state and action and may be driven by the fact that fully observable physics-based simulation environments entail deterministic transition dynamics.
Benchmarks for Deep Off-Policy Evaluation
Off-policy evaluation (OPE) holds the promise of being able to leverage large, offline datasets for both evaluating and selecting complex policies for decision making.
Hyperparameter Selection for Offline Reinforcement Learning
Therefore, in this work, we focus on \textit{offline hyperparameter selection}, i. e. methods for choosing the best policy from a set of many policies trained using different hyperparameters, given only logged data.
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning
We hope that our suite of benchmarks will increase the reproducibility of experiments and make it possible to study challenging tasks with a limited computational budget, thus making RL research both more systematic and more accessible across the community.
Acme: A Research Framework for Distributed Reinforcement Learning
Ultimately, we show that the design decisions behind Acme lead to agents that can be scaled both up and down and that, for the most part, greater levels of parallelization result in agents with equivalent performance, just faster.
Improving the Gating Mechanism of Recurrent Neural Networks
Gating mechanisms are widely used in neural network models, where they allow gradients to backpropagate more easily through depth or time.
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
This paper introduces R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial conditions.
One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL
MetaMimic can learn both (i) policies for high-fidelity one-shot imitation of diverse novel skills, and (ii) policies that enable the agent to solve tasks more efficiently than the demonstrators.
Playing hard exploration games by watching YouTube
One successful method of guiding exploration in these domains is to imitate trajectories provided by a human demonstrator.
Fast Generation for Convolutional Autoregressive Models
In this work, we describe a method to speed up generation in convolutional autoregressive models.
Fast Wavenet Generation Algorithm
This paper presents an efficient implementation of the Wavenet generation process called Fast Wavenet.
Seq-NMS for Video Object Detection
Video object detection is challenging because objects that are easily detected in one frame may be difficult to detect in another frame within the same clip.
How Deep Neural Networks Can Improve Emotion Recognition on Video Data
In this work, we present a system that performs emotion recognition on video data using both CNNs and RNNs, and we also analyze how much each neural network component contributes to the system's overall performance.
Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?
Despite being the appearance-based classifier of choice in recent years, relatively few works have examined how much convolutional neural networks (CNNs) can improve performance on accepted expression recognition benchmarks and, more importantly, examine what it is they actually learn.
An Analysis of Unsupervised Pre-training in Light of Recent Advances
We discover unsupervised pre-training, as expected, helps when the ratio of unsupervised to supervised samples is high, and surprisingly, hurts when the ratio is low.
Ranked #87 on
Image Classification
on STL-10
