Reinforcement Learning
Evolution-Guided Policy Gradient in Reinforcement Learning
Temporal Difference methods inRL use bootstrapping to address this issue but often struggle when the time horizons are long and the reward is sparse.
Adversarial Attacks on Stochastic Bandits
Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Jerry Zhu
Neural Information Processing Systems http://nips.cc/
ZSC-Eval: An Evaluation Toolkit and Benchmark for Multi-agent Zero-shot Coordination Xihuai Wang
The significant difference between the deployment-time partners' distribution and the training partners' distribution determined by the
Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator
Karl Krauth, Stephen Tu, Benjamin Recht
Neural Information Processing Systems http://nips.cc/
Distributed Multitask Reinforcement Learning with Quadratic Convergence
Rasul Tutunov, Dongho Kim, Haitham Bou Ammar
Multitask reinforcement learning (MTRL) suffers from scalability issues when the number of tasks or trajectories grows large.
Almost Horizon-Free Structure-Aware Best Policy Identification with a Generative Model
Andrea Zanette, Mykel J. Kochenderfer, Emma Brunskill
Inparticular,well knownbounds foronline learningscale as a function of the gap between the expected reward of a particular action and the optimalaction [ABF02] and also on the variance ofthe rewards [AMS09].
Inequity aversion improves cooperation in intertemporal social dilemmas
Edward Hughes, Joel Z. Leibo, Matthew Phillips, Karl Tuyls, Edgar Dueรฑez-Guzman, Antonio Garcรญa Castaรฑeda, Iain Dunning, Tina Zhu, Kevin McKee, Raphael Koster, Heather Roff, Thore Graepel
Neural Information Processing Systems http://nips.cc/
Visual Reinforcement Learning with Imagined Goals
Ashvin V. Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, Sergey Levine
Furthermore, toprovide therequisite levelofgenerality,these skills must handle raw sensory input such as images.