Reinforcement Learning
Offline Constrained Multi-Objective Reinforcement Learning via Pessimistic Dual Value Iteration
We also specialize PEDI to the setting with linear function approximation.
Machine versus Human Attention in Deep Reinforcement Learning Tasks
Deep reinforcement learning (RL) algorithms are powerful tools for solving visuo-motor decision tasks.
Robust exploration in linear quadratic reinforcement learning
Jack Umenberger, Mina Ferizbegovic, Thomas B. Schรถn, Hรฅkan Hjalmarsson
Neural Information Processing Systems http://nips.cc/
Learning to Synthesize Programs as Interpretable and Generalizable Policies Dweep Trivedi Jesse Zhang 1 Shao-Hua Sun 1 Joseph J. Lim 1 1 University of Southern California
Y et, these works either employ limited policy representations (e.g.
_NeurIPS_2022__On_the_Effectiveness_of_Fine_tuning_Versus_Meta_reinforcement_Learning (1)
Meta-learning approaches have emerged as a popular solution to achieve this. However, meta-reinforcement learning (meta-RL) algorithms have thus far been predominately validated on simple environments with narrow task distributions.
