Search Results for author:
Found 7 papers, 0 papers with code
OLLIE: Imitation Learning from Offline Pretraining to Online Finetuning
In this paper, we study offline-to-online Imitation Learning (IL) that pretrains an imitation policy from static demonstration data, followed by fast finetuning with minimal environmental interaction.
How to Leverage Diverse Demonstrations in Offline Imitation Learning
Offline Imitation Learning (IL) with imperfect demonstrations has garnered increasing attention owing to the scarcity of expert data in many real-world domains.
Federated Offline Policy Optimization with Dual Regularization
Federated Reinforcement Learning (FRL) has been deemed as a promising solution for intelligent decision-making in the era of Artificial Internet of Things.
Momentum-Based Federated Reinforcement Learning with Interaction and Communication Efficiency
Federated Reinforcement Learning (FRL) has garnered increasing attention recently.
CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning
This work aims to tackle a major challenge in offline Inverse Reinforcement Learning (IRL), namely the reward extrapolation error, where the learned reward function may fail to explain the task correctly and misguide the agent in unseen environments due to the intrinsic covariate shift.
Efficient Federated Meta-Learning over Multi-Access Wireless Networks
After that, we formulate a resource allocation problem integrating NUFM in multi-access wireless systems to jointly improve the convergence rate and minimize the wall-clock time along with energy cost.
Inexact-ADMM Based Federated Meta-Learning for Fast and Continual Edge Learning
To overcome these challenges, we explore continual edge learning capable of leveraging the knowledge transfer from previous tasks.
