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Found 10 papers, 4 papers with code
A Survey on Transformers in Reinforcement Learning
Transformer has been considered the dominating neural architecture in NLP and CV, mostly under supervised settings.
Flow to Control: Offline Reinforcement Learning with Lossless Primitive Discovery
We show that such lossless primitives can drastically improve the performance of hierarchical policies.
Improving Graph-Based Text Representations with Character and Word Level N-grams
Graph-based text representation focuses on how text documents are represented as graphs for exploiting dependency information between tokens and documents within a corpus.
Latent-Variable Advantage-Weighted Policy Optimization for Offline RL
Offline reinforcement learning methods hold the promise of learning policies from pre-collected datasets without the need to query the environment for new transitions.
Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RL
In this paper, we revisit the theoretical property of GCSL -- optimizing a lower bound of the goal reaching objective, and extend GCSL as a novel offline goal-conditioned RL algorithm.
Estimating High Order Gradients of the Data Distribution by Denoising
By leveraging Tweedie's formula on higher order moments, we generalize denoising score matching to estimate higher order derivatives.
Offline Reinforcement Learning with Reverse Model-based Imagination
These reverse imaginations provide informed data augmentation for model-free policy learning and enable conservative generalization beyond the offline dataset.
Tractable Computation of Expected Kernels
Computing the expectation of kernel functions is a ubiquitous task in machine learning, with applications from classical support vector machines to exploiting kernel embeddings of distributions in probabilistic modeling, statistical inference, causal discovery, and deep learning.
Unifying Local and Global Change Detection in Dynamic Networks
Many real-world networks are complex dynamical systems, where both local (e. g., changing node attributes) and global (e. g., changing network topology) processes unfold over time.
Scalable MCMC for Mixed Membership Stochastic Blockmodels
We propose a stochastic gradient Markov chain Monte Carlo (SG-MCMC) algorithm for scalable inference in mixed-membership stochastic blockmodels (MMSB).
