Search Results for author:
Found 6 papers, 1 papers with code
Exploiting Generalization in Offline Reinforcement Learning via Unseen State Augmentations
Offline reinforcement learning (RL) methods strike a balance between exploration and exploitation by conservative value estimation -- penalizing values of unseen states and actions.
Model-Advantage and Value-Aware Models for Model-Based Reinforcement Learning: Bridging the Gap in Theory and Practice
This work shows that value-aware model learning, known for its numerous theoretical benefits, is also practically viable for solving challenging continuous control tasks in prevalent model-based reinforcement learning algorithms.
Bridging Worlds in Reinforcement Learning with Model-Advantage
Despite the breakthroughs achieved by Reinforcement Learning (RL) in recent years, RL agents often fail to perform well in unseen environments.
DS-VIC: Unsupervised Discovery of Decision States for Transfer in RL
We learn to identify decision states, namely the parsimonious set of states where decisions meaningfully affect the future states an agent can reach in an environment.
IR-VIC: Unsupervised Discovery of Sub-goals for Transfer in RL
We propose a novel framework to identify sub-goals useful for exploration in sequential decision making tasks under partial observability.
Scalable Generative Models for Multi-label Learning with Missing Labels
We present a scalable, generative framework for multi-label learning with missing labels.
