261 citations · 465 across the 10 of their papers we have counts for
6 papers · 1 filter
Mastering Atari Games with Limited Data
Weirui Ye, Shaohuai Liu, Thanard Kurutach +2
Reinforcement learning has achieved great success in many applications. However, sample efficiency remains a key challenge, with prominent methods requiring millions (or even billi…
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning
Younggyo Seo, Kimin Lee, Ignasi Clavera +3
Model-based reinforcement learning (RL) has shown great potential in various control tasks in terms of both sample-efficiency and final performance. However, learning a generalizab…
Sparse Graphical Memory for Robust Planning
Scott Emmons, Ajay Jain, Michael Laskin +3
To operate effectively in the real world, agents should be able to act from high-dimensional raw sensory input such as images and achieve diverse goals across long time-horizons. C…
Deep Variational Semi-Supervised Novelty Detection
Tal Daniel, Thanard Kurutach, Aviv Tamar
In anomaly detection (AD), one seeks to identify whether a test sample is abnormal, given a data set of normal samples. A recent and promising approach to AD relies on deep generat…
Learning Plannable Representations with Causal InfoGAN
Thanard Kurutach, Aviv Tamar, Ge Yang +2
In recent years, deep generative models have been shown to 'imagine' convincing high-dimensional observations such as images, audio, and even video, learning directly from raw data…
Model-Ensemble Trust-Region Policy Optimization
Thanard Kurutach, Ignasi Clavera, Yan Duan +2
Model-free reinforcement learning (RL) methods are succeeding in a growing number of tasks, aided by recent advances in deep learning. However, they tend to suffer from high sample…