5 papers · 1 filter
Wavelet Predictive Representations for Non-Stationary Reinforcement Learning
Min Wang, Xin Li, Ye He +4
The real world is inherently non-stationary, with ever-changing factors, such as weather conditions and traffic flows, making it challenging for agents to adapt to varying environm…
Learning Fused State Representations for Control from Multi-View Observations
Zeyu Wang, Yao-Hui Li, Xin Li +3
Multi-View Reinforcement Learning (MVRL) seeks to provide agents with multi-view observations, enabling them to perceive environment with greater effectiveness and precision. Recen…
PcLast: Discovering Plannable Continuous Latent States
Anurag Koul, Shivakanth Sujit, Shaoru Chen +11
Goal-conditioned planning benefits from learned low-dimensional representations of rich observations. While compact latent representations typically learned from variational autoen…
Learning Latent Dynamic Robust Representations for World Models
Ruixiang Sun, Hongyu Zang, Xin Li +1
Visual Model-Based Reinforcement Learning (MBRL) promises to encapsulate agent's knowledge about the underlying dynamics of the environment, enabling learning a world model as a us…
Generalizing Multi-Step Inverse Models for Representation Learning to Finite-Memory POMDPs
Lili Wu, Ben Evans, Riashat Islam +3
Discovering an informative, or agent-centric, state representation that encodes only the relevant information while discarding the irrelevant is a key challenge towards scaling rei…