X
Xi Chen
Researcher at University of California, Berkeley
Publications - 53
Citations - 26834
Xi Chen is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Reinforcement learning & Autoregressive model. The author has an hindex of 39, co-authored 53 publications receiving 22393 citations.
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#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
Haoran Tang,Rein Houthooft,Davis Foote,Adam Stooke,Xi Chen,Yan Duan,John Schulman,Filip De Turck,Pieter Abbeel +8 more
TL;DR: A simple generalization of the classic count-based approach can reach near state-of-the-art performance on various high-dimensional and/or continuous deep RL benchmarks, and is found that simple hash functions can achieve surprisingly good results on many challenging tasks.
Posted Content
Variational Lossy Autoencoder
Xi Chen,Diederik P. Kingma,Tim Salimans,Yan Duan,Prafulla Dhariwal,John Schulman,Ilya Sutskever,Pieter Abbeel +7 more
TL;DR: Li et al. as mentioned in this paper combine VAE with neural autoregressive models such as RNN, MADE and PixelRNN/CNN to learn a global representation for 2D images that describes only global structure and discards information about detailed texture.
Proceedings Article
Variational Lossy Autoencoder
Xi Chen,Diederik P. Kingma,Tim Salimans,Yan Duan,Prafulla Dhariwal,John Schulman,Ilya Sutskever,Pieter Abbeel +7 more
TL;DR: This paper presents a simple but principled method to learn global representations by combining Variational Autoencoder (VAE) with neural autoregressive models such as RNN, MADE and PixelRNN/CNN with greatly improve generative modeling performance of VAEs.
Posted Content
Parameter Space Noise for Exploration
Matthias Plappert,Rein Houthooft,Prafulla Dhariwal,Szymon Sidor,Richard Chen,Xi Chen,Tamim Asfour,Pieter Abbeel,Marcin Andrychowicz +8 more
TL;DR: In this article, the authors combine parameter noise with traditional RL methods to combine the best of both worlds, and demonstrate that both off-and on-policy methods benefit from this approach through experimental comparison of DQN, DDPG, and TRPO on high-dimensional discrete action environments as well as continuous control tasks.
Proceedings Article
#Exploration: a study of count-based exploration for deep reinforcement learning
Haoran Tang,Rein Houthooft,Davis Foote,Adam Stooke,Xi Chen,Yan Duan,John Schulman,Filip De Turck,Pieter Abbeel +8 more
TL;DR: In this article, a simple generalization of the classic count-based approach can reach near state-of-the-art performance on various high-dimensional and/or continuous deep RL benchmarks.