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Found 4 papers, 1 papers with code
Dynamic Regret Analysis of Safe Distributed Online Optimization for Convex and Non-convex Problems
We prove that for convex functions, D-Safe-OGD achieves a dynamic regret bound of $O(T^{2/3} \sqrt{\log T} + T^{1/3}C_T^*)$, where $C_T^*$ denotes the path-length of the best minimizer sequence.
Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward Environments
Meta reinforcement learning (Meta-RL) is an approach wherein the experience gained from solving a variety of tasks is distilled into a meta-policy.
Safe Online Convex Optimization with Unknown Linear Safety Constraints
We study the problem of safe online convex optimization, where the action at each time step must satisfy a set of linear safety constraints.
Smooth Imitation Learning via Smooth Costs and Smooth Policies
We call our new smooth IL algorithm \textit{Smooth Policy and Cost Imitation Learning} (SPaCIL, pronounced 'Special').
