Summary of Learning Successor Features the Simple Way, by Raymond Chua et al.
Learning Successor Features the Simple Wayby Raymond Chua, Arna Ghosh, Christos Kaplanis, Blake A. Richards,…
Learning Successor Features the Simple Wayby Raymond Chua, Arna Ghosh, Christos Kaplanis, Blake A. Richards,…
Robot Policy Learning with Temporal Optimal Transport Rewardby Yuwei Fu, Haichao Zhang, Di Wu, Wei…
Human-Readable Programs as Actors of Reinforcement Learning Agents Using Critic-Moderated Evolutionby Senne Deproost, Denis Steckelmacher,…
Sequential choice in ordered bundlesby Rajeev Kohli, Kriste Krstovski, Hengyu Kuang, Hengxu LinFirst submitted to…
Stochastic Approximation with Unbounded Markovian Noise: A General-Purpose Theoremby Shaan Ul Haque, Siva Theja MaguluriFirst…
A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applicationsby Sriniketh Vangaru, Daniel Rosen, Dylan Green,…
The Limits of Transfer Reinforcement Learning with Latent Low-rank Structureby Tyler Sam, Yudong Chen, Christina…
Identifying Selections for Unsupervised Subtask Discoveryby Yiwen Qiu, Yujia Zheng, Kun ZhangFirst submitted to arxiv…
FALCON: Feedback-driven Adaptive Long/short-term memory reinforced Coding Optimization systemby Zeyuan Li, Yangfan He, Lewei He,…
Unveiling the Role of Expert Guidance: A Comparative Analysis of User-centered Imitation Learning and Traditional…