Reinforcement Learning
eb3c8135137c8a60425a0320869ad87e-Paper-Conference.pdf
Recently, reinforcement learning (RL) based approaches have attracted increasing attention for dynamic resource management asRLhelpsautomatically adapttoaspecific userworkload.
NearOptimalExploration-Exploitationin Non-CommunicatingMarkovDecisionProcesses
Reinforcement learning (RL) [1] studies the problem of learning in sequential decision-making problems where the dynamics of the environment is unknown, but can be learnt by performing actions andobserving their outcome inanonline fashion. Asample-efficient RLagent must trade off the explorationneeded to collect information about the environment, and theexploitation of the experience gathered so far to gain as much reward as possible.
Fast Efficient Hyperparameter Tuning for Policy Gradient Methods
Supratik Paul, Vitaly Kurin, Shimon Whiteson
Neural Information Processing Systems http://nips.cc/
Playing hard exploration games by watching YouTube
Yusuf Aytar, Tobias Pfaff, David Budden, Thomas Paine, Ziyu Wang, Nando de Freitas
Deep reinforcement learning methods traditionally struggle withtaskswhere environment rewards are particularly sparse.
Large Language Models Can Implement Policy Iteration Ethan Brooks 1, Logan Walls 2, Richard L. Lewis
Gradient techniques are inherently slow, sacrificing the "few-shot" quality
Constrained Cross-Entropy Method for Safe Reinforcement Learning
Neural Information Processing Systems http://nips.cc/