Reinforcement Learning
Reinforcement Learning with Convex Constraints
In standard reinforcement learning (RL), a learning agent seeks to optimize the overall reward. However, many key aspects of a desired behavior are more naturally expressed as constraints.
On Learning Intrinsic Rewards for Policy Gradient Methods
Zeyu Zheng, Junhyuk Oh, Satinder Singh
Whether itispossible tolearn intrinsic reward functions for learning agents remains an open problem.
A Lyapunov-based Approach to Safe Reinforcement Learning
Yinlam Chow, Ofir Nachum, Edgar Duenez-Guzman, Mohammad Ghavamzadeh
Neural Information Processing Systems http://nips.cc/
A Block Coordinate Ascent Algorithm for Mean-Variance Optimization
Tengyang Xie, Bo Liu, Yangyang Xu, Mohammad Ghavamzadeh, Yinlam Chow, Daoming Lyu, Daesub Yoon
Risk management in dynamic decision problems is a primary concern in many fields, including financial investment, autonomous driving, and healthcare.
Curriculum-guided Hindsight Experience Replay
Meng Fang, Tianyi Zhou, Yali Du, Lei Han, Zhengyou Zhang
Neural Information Processing Systems http://nips.cc/
Temporal Regularization for Markov Decision Process
Pierre Thodoroff, Audrey Durand, Joelle Pineau, Doina Precup
Yetinreinforcementlearning,duetothenatureofthe Bellman equation, there isanopportunity toalsoexploit temporal regularization based on smoothness in value estimates over trajectories. This paper explores a class of methods for temporal regularization.
Exponentially Weighted Imitation Learning for Batched Historical Data
Qing Wang, Jiechao Xiong, Lei Han, peng sun, Han Liu, Tong Zhang
We consider deep policy learning with only batched historical trajectories. The main challenge of this problem is that the learner no longer has a simulator or "environment oracle" as in most reinforcement learning settings.