schulman
The Importance of Sampling inMeta-Reinforcement Learning
Bradly Stadie, Ge Yang, Rein Houthooft, Peter Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever
This sampling process is embodied as the policyπ, which is responsible for outputting an actiona conditioned on past environmental states{s}.
Online Robust Policy Learning in the Presence of Unknown Adversaries
Aaron Havens, Zhanhong Jiang, Soumik Sarkar
Lemma 2. Let Assumption 1 hold.
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.
CoresetforLine-SetsClustering
A natural generalization is to replace this input setP of n points by a setP of n sets inX. The distance from such an input setP P to a setC of centers can then be defined as the distance between the closest point-center pair. This problem is calledk-mean for sets; see e.g.
k-Means Clustering of Lines for Big Data
Neural Information Processing Systems http://nips.cc/
Learning Robust Options by Conditional Value at Risk Optimization
Takuya Hiraoka, Takahisa Imagawa, Tatsuya Mori, Takashi Onishi, Yoshimasa Tsuruoka
In the reinforcement learning context, anOption means a temporally extended sequence of actions [30],andisregarded asuseful formanypurposes, such asspeeding uplearning, transferring skills across domains, and solving long-term planning problems.
TorsionNet: AReinforcementLearningApproachto SequentialConformerSearch
Accurate prediction of likely 3D geometries of flexiblemolecules is along standing goal of computational chemistry, with broad implications for drug design, biopolymer research, and QSAR analysis.
LifelongPolicyGradientLearning ofFactoredPolicies forFasterTrainingWithoutForgetting
We provide a novel method for lifelong policy gradient learning that trains lifelong function approximators directly via policygradients, allowing the agent to benefit from accumulated knowledge throughout the entire training process.
