17 citations · 36 across the 8 of their papers we have counts for
6 papers · 1 filter
Transfer RL across Observation Feature Spaces via Model-Based Regularization
Yanchao Sun, Ruijie Zheng, Xiyao Wang +2
In many reinforcement learning (RL) applications, the observation space is specified by human developers and restricted by physical realizations, and may thus be subject to dramati…
On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning
Andrew Cohen, Ervin Teng, Vincent-Pierre Berges +5
The creation and destruction of agents in cooperative multi-agent reinforcement learning (MARL) is a critically under-explored area of research. Current MARL algorithms often assum…
Generating Multi-type Temporal Sequences to Mitigate Class-imbalanced Problem
Lun Jiang, Nima Salehi Sadghiani, Zhuo Tao +1
From the ad network standpoint, a user's activity is a multi-type sequence of temporal events consisting of event types and time intervals. Understanding user patterns in ad networ…
Maximum Entropy Diverse Exploration: Disentangling Maximum Entropy Reinforcement Learning
Andrew Cohen, Lei Yu, Xingye Qiao +1
Two hitherto disconnected threads of research, diverse exploration (DE) and maximum entropy RL have addressed a wide range of problems facing reinforcement learning algorithms via…
Diverse Exploration via Conjugate Policies for Policy Gradient Methods
Andrew Cohen, Xingye Qiao, Lei Yu +2
We address the challenge of effective exploration while maintaining good performance in policy gradient methods. As a solution, we propose diverse exploration (DE) via conjugate po…
Diverse Exploration for Fast and Safe Policy Improvement
Andrew Cohen, Lei Yu, Robert Wright
We study an important yet under-addressed problem of quickly and safely improving policies in online reinforcement learning domains. As its solution, we propose a novel exploration…