Reinforcement Learning
Bootstrapped Transformer for Offline Reinforcement Learning Kerong Wang Shanghai Jiao Tong University Hanye Zhao Shanghai Jiao Tong University Xufang Luo Microsoft Research Asia Kan Ren
The work was conducted during the internship of Kerong Wang and Hanye Zhao at Microsoft Research.
Scalable Coordinated Exploration in Concurrent Reinforcement Learning
Maria Dimakopoulou, Ian Osband, Benjamin Van Roy
Neural Information Processing Systems http://nips.cc/
Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards
Alexander Trott, Stephan Zheng, Caiming Xiong, Richard Socher
Neural Information Processing Systems http://nips.cc/
Learning to Share and Hide Intentions using Information Regularization
DJ Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matt Botvinick, David J. Schwab
Neural Information Processing Systems http://nips.cc/
ContrastiveIntrinsicControlforUnsupervised ReinforcementLearning
Unlikeknowledge-based anddata-basedalgorithms, competence-based algorithms simultaneously address both the exploration challenge as well as distilling the generated experience in the form of reusable skills.
EffectsofSafetyStateAugmentationon SafeExploration
There are still, however, some unsolved challenges for a successful deployment of RL such as efficient learning of constrained or safe Markov Decision Processes (MDPs) [4].
PALMER: Perception-ActionLoopwithMemory forLong-HorizonPlanning
This creates a tight feedback loop between representation learning,memory,reinforcementlearning,andsampling-basedplanning.