State Representation Learning Using an Unbalanced Atlas
A manifold-based self-supervised learning paradigm that scales state representations to high-dimensional target spaces.
Self-supervised learning, Manifold learning, Reinforcement learning
Papers and preprints.
Work on representation learning and reinforcement learning, with code where it exists. For citation counts and the most current index, see Google Scholar or DBLP.
2024
Maximum Manifold Capacity Representations in State Representation Learning
Extends manifold-aware state representation learning with a nuclear-norm objective and a more efficient route to manifold-capacity regularization.
Manifold capacity, Representation learning, Atari
A Manifold Representation of the Key in Vision Transformers
Studies decoupled query–key–value construction and introduces manifold structure into the key representation of vision transformers.
Vision transformers, Manifold learning, Attention
2023
Unsupervised State Representation Learning in Partially Observable Atari Games
Develops an unsupervised state representation scheme for partially observable environments and evaluates it on Atari-based benchmarks.
Partial observability, State representation, Contrastive learning
2022
Improving the Diversity of Bootstrapped DQN by Replacing Priors With Noise
Replaces fixed randomized priors with Gaussian noise to sustain ensemble diversity and improve deep exploration in Atari games.
Deep reinforcement learning, Exploration, DQN
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples
Incorporates coarse expert state-value information into deep Q-learning to guide online learning from limited demonstrations.
Expert knowledge, Q-learning, Offline examples
Deep Reinforcement Learning with Swin Transformers
Investigates Swin Transformer backbones for online deep reinforcement learning across a large suite of Atari games.
Swin Transformer, Deep RL, Visual learning