(This contents of this NIPS spotlight video is similar to the post below, although the post is a bit more detailed.) Reinforcement learning agents can learn to play video games (for instance Atari games) by themselves. The original DQN algorithm and many of its successors clip the rewards they receive while learning. This helps stabilize the deep learning,…
Category: Papers
Learning values across many orders of magnitude
Our paper about adaptive target normalization in deep learning was accepted at NIPS 2016. A preprint can be found on arXiv.org. The abstract and a more informal summary can be found below. Update: There are now videos of the effect of the new approach on Atari. Abstract Most learning algorithms are not invariant to the scale of the function that…
Best paper at ICML: Dueling Network Architectures for Deep Reinforcement Learning
Today Ziyu Wang will present our paper on dueling network architectures for deep reinforcement learning at the international conference for machine learning (ICML) in New York. This paper received the best paper award. The elegant main idea of the paper is to separate the value of a state and the advantage value for each action in that state….
Deep Reinforcement Learning with Double Q-learning
We recently published a paper on deep reinforcement learning with Double Q-learning, demonstrating that Q-learning learns overoptimistic action values when combined with deep neural networks, even on deterministic environments such as Atari video games, and that this can be remedied by using a variant of Double Q-learning. The resulting Double DQN algorithm greatly improves over the performance of the DQN algorithm. Abstract:…
Weighted importance sampling for off-policy learning with linear function approximation
The following paper was presented at NIPS 2014: A. Rupam Mahmood, Hado van Hasselt, and Richard S. Sutton (2014). “Weighted importance sampling for off-policy learning with linear function approximation.” Advances in Neural Information Processing Systems 27. Abstract: Importance sampling is an essential component of off-policy model-free reinforcement learning algorithms. However, its most effective variant, weighted…
Hado van Hasselt 