5 papers
Latent Wasserstein Adversarial Imitation Learning
Siqi Yang, Kai Yan, Alexander G. Schwing +1
Imitation Learning (IL) enables agents to mimic expert behavior by learning from demonstrations. However, traditional IL methods require large amounts of medium-to-high-quality dem…
Reinforcement Learning Gradients as Vitamin for Online Finetuning Decision Transformers
Kai Yan, Alexander G. Schwing, Yu-Xiong Wang
Decision Transformers have recently emerged as a new and compelling paradigm for offline Reinforcement Learning (RL), completing a trajectory in an autoregressive way. While improv…
A Simple Solution for Offline Imitation from Observations and Examples with Possibly Incomplete Trajectories
Kai Yan, Alexander G. Schwing, Yu-Xiong Wang
Offline imitation from observations aims to solve MDPs where only task-specific expert states and task-agnostic non-expert state-action pairs are available. Offline imitation is us…
Offline Imitation from Observation via Primal Wasserstein State Occupancy Matching
Kai Yan, Alexander G. Schwing, Yu-xiong Wang
In real-world scenarios, arbitrary interactions with the environment can often be costly, and actions of expert demonstrations are not always available. To reduce the need for both…
Robust Model-Based Optimization for Challenging Fitness Landscapes
Saba Ghaffari, Ehsan Saleh, Alexander G. Schwing +3
Protein design, a grand challenge of the day, involves optimization on a fitness landscape, and leading methods adopt a model-based approach where a model is trained on a training…