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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.
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