2 citations · 6 across the 5 of their papers we have counts for
5 papers · 1 filter
Towards robust and domain agnostic reinforcement learning competitions
William Hebgen Guss, Stephanie Milani, Nicholay Topin +26
Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field. Desp…
Discriminator Soft Actor Critic without Extrinsic Rewards
Daichi Nishio, Daiki Kuyoshi, Toi Tsuneda +1
It is difficult to be able to imitate well in unknown states from a small amount of expert data and sampling data. Supervised learning methods such as Behavioral Cloning do not req…
Online Heterogeneous Mixture Learning for Big Data
Kazuki Seshimo, Ota Akira, Nishio Daichi +1
We propose the online machine learning for big data analysis with heterogeneity. We performed an experiment to compare the accuracy of each iteration between batch one and online o…
Random Projection in Neural Episodic Control
Daichi Nishio, Satoshi Yamane
End-to-end deep reinforcement learning has enabled agents to learn with little preprocessing by humans. However, it is still difficult to learn stably and efficiently because the l…
Faster Deep Q-learning using Neural Episodic Control
Daichi Nishio, Satoshi Yamane
The research on deep reinforcement learning which estimates Q-value by deep learning has been attracted the interest of researchers recently. In deep reinforcement learning, it is…