Reinforcement Learning
Speaker-Follower Models for Vision-and-Language Navigation Daniel Fried
Navigation guided by natural language instructions presents a challenging reasoning problem for instruction followers.
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning
Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J. Mankowitz, Shie Mannor
Action Elimination Network (AEN) that eliminates sub-optimal actions.
On Oracle-Efficient PAC RL with Rich Observations
Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
Neural Information Processing Systems http://nips.cc/
Learning Temporal Point Processes via Reinforcement Learning
Shuang Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie, Le Song
Social goods, such as healthcare, smart city, and information networks, often produce ordered event data in continuous time.
Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making
Nishant Desai, Andrew Critch, Stuart J. Russell
Harsanyi's theorem shows that when the principals have a
Randomized Prior Functions for Deep Reinforcement Learning
Ian Osband, John Aslanides, Albin Cassirer
Dealing with uncertainty is essential for e cient 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