Reinforcement Learning
DAC: The Double Actor-Critic Architecture for Learning Options
Shangtong Zhang, Shimon Whiteson
Temporal abstraction (i.e., hierarchy) is a key component in reinforcement learning (RL).
Value Function in Frequency Domain and the Characteristic Value Iteration Algorithm
Neural Information Processing Systems http://nips.cc/
On the Theory of Reinforcement Learning with Once-per-Episode Feedback
This formulation of RL has had significant empirical success in the recent past [24, 23, 33, 32].
A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation
Runzhe Yang, Xingyuan Sun, Karthik Narasimhan
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
Neural Information Processing Systems http://nips.cc/
A Kernel Loss for Solving the Bellman Equation
Yihao Feng, Lihong Li, Qiang Liu
Neural Information Processing Systems http://nips.cc/
Neural Dynamic Policies for End-to-End Sensorimotor Learning
The current dominant paradigm in sensorimotor control, whether imitation or reinforcement learning, is to train policies directly in raw action spaces such as torque, joint angle, or end-effector position.
Provably Efficient Q-Learning with Low Switching Cost
Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang
Neural Information Processing Systems http://nips.cc/
