Search Results for author:
Found 4 papers, 2 papers with code
Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation and Human Feedback
Risk-sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk.
Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement Learning
Multi-user delay constrained scheduling is important in many real-world applications including wireless communication, live streaming, and cloud computing.
Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation
We study reinforcement learning with linear function approximation where the transition probability and reward functions are linear with respect to a feature mapping $\boldsymbol{\phi}(s, a)$.
RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch
Training deep reinforcement learning (DRL) models usually requires high computation costs.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
