Reinforcement Learning
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization
Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong
We prove that the proposed algorithm converges to the optimal solution at a global geometric rate.
Bayesian Adversarial Learning
Neural Information Processing Systems http://nips.cc/
On Learning Intrinsic Rewards for Policy Gradient Methods
Zeyu Zheng, Junhyuk Oh, Satinder Singh
In this paper we build on the Optimal Rewards Framework of Singh et al. [2010] that defines the optimal intrinsic reward function as one that when used by an RL agent achieves behavior that optimizes the
A Lyapunov-based Approach to Safe Reinforcement Learning
Yinlam Chow, Ofir Nachum, Edgar Duenez-Guzman, Mohammad Ghavamzadeh
In many real-world reinforcement learning (RL) problems, besides optimizing the main objective function, an agent must concurrently avoid violating a number of constraints. In particular, besides optimizing performance, it is crucial to guarantee the safety of an agent during training as well as deployment (e.g., a robot should avoid taking actions - exploratory or not - which irrevocably harm its hardware). To incorporate safety in RL, we derive algorithms under the framework of constrained Markov decision processes (CMDPs), an extension of the standard Markov decision processes (MDPs) augmented with constraints on expected cumulative costs.
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.
Temporal Regularization for Markov Decision Process
Pierre Thodoroff, Audrey Durand, Joelle Pineau, Doina Precup
Neural Information Processing Systems http://nips.cc/
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.
Teaching Inverse Reinforcement Learners via Features and Demonstrations
Luis Haug, Sebastian Tschiatschek, Adish Singla
Neural Information Processing Systems http://nips.cc/
An Off-policy Policy Gradient Theorem Using Emphatic Weightings
Ehsan Imani, Eric Graves, Martha White
Neural Information Processing Systems http://nips.cc/
Constrained Cross-Entropy Method for Safe Reinforcement Learning
We study a safe reinforcement learning problem in which the constraints are defined as the expected cost over finite-length trajectories.