3 citations · 7 across the 8 of their papers we have counts for
6 papers · 1 filter
Maximum Manifold Capacity Representations in State Representation Learning
Li Meng, Morten Goodwin, Anis Yazidi +1
The expanding research on manifold-based self-supervised learning (SSL) builds on the manifold hypothesis, which suggests that the inherent complexity of high-dimensional data can…
State Representation Learning Using an Unbalanced Atlas
Li Meng, Morten Goodwin, Anis Yazidi +1
The manifold hypothesis posits that high-dimensional data often lies on a lower-dimensional manifold and that utilizing this manifold as the target space yields more efficient repr…
Unsupervised Representation Learning in Partially Observable Atari Games
Li Meng, Morten Goodwin, Anis Yazidi +1
State representation learning aims to capture latent factors of an environment. Contrastive methods have performed better than generative models in previous state representation le…
Deep Reinforcement Learning with Swin Transformers
Li Meng, Morten Goodwin, Anis Yazidi +1
Transformers are neural network models that utilize multiple layers of self-attention heads and have exhibited enormous potential in natural language processing tasks. Meanwhile, t…
Improving the Diversity of Bootstrapped DQN by Replacing Priors With Noise
Li Meng, Morten Goodwin, Anis Yazidi +1
Q-learning is one of the most well-known Reinforcement Learning algorithms. There have been tremendous efforts to develop this algorithm using neural networks. Bootstrapped Deep Q-…
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples
Li Meng, Anis Yazidi, Morten Goodwin +1
In this article, we propose a novel algorithm for deep reinforcement learning named Expert Q-learning. Expert Q-learning is inspired by Dueling Q-learning and aims at incorporating…