Technology
Ordering-based Conditions for Global Convergence of Policy Gradient Methods
The conditions on the representation that imply global convergence are different between these two algorithms.
NeuralSplineFlows
Explicit density evaluation is required in many statistical procedures, while synthesis of novel examples can enable agents to imagine and plan in an environment prior tochoosing aaction.
Scaling the Poisson GLM to massive neural datasets through polynomial approximations
David Zoltowski, Jonathan W. Pillow
Neural Information Processing Systems http://nips.cc/
Object-CategoryAwareReinforcementLearning
Reinforcement Learning (RL) has achievedimpressiveprogress inrecent years, such asresults in Atari [24] and Go [28] in which RL agents even perform better than human beings.
Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective
Self-supervised heterogeneous graph learning (SHGL) has shown promising potential in diverse scenarios.
Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning
Yonathan Efroni, Gal Dalal, Bruno Scherrer, Shie Mannor
Our framework is the infinite-horizon discounted Markov Decision Process (MDP).
7a9a322cbe0d06a98667fdc5160dc6f8-AuthorFeedback.pdf
In [1], all previous data is available and, as such, there is no issue of negative backward transfer ("...we sidestep25 the problem of catastrophic forgetting by maintaining a buffer of all the observed data" [pg. 4 of1]).