17 citations · 38 across the 7 of their papers we have counts for
7 papers
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Megan M. Baker, Alexander New, Mario Aguilar-Simon +44
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks enc…
Lifelong Machine Learning of Functionally Compositional Structures
Jorge A. Mendez
A hallmark of human intelligence is the ability to construct self-contained chunks of knowledge and reuse them in novel combinations for solving different problems. Learning such c…
CompoSuite: A Compositional Reinforcement Learning Benchmark
Jorge A. Mendez, Marcel Hussing, Meghna Gummadi +1
We present CompoSuite, an open-source simulated robotic manipulation benchmark for compositional multi-task reinforcement learning (RL). Each CompoSuite task requires a particular…
Reinforcement Learning of Multi-Domain Dialog Policies Via Action Embeddings
Jorge A. Mendez, Alborz Geramifard, Mohammad Ghavamzadeh +1
Learning task-oriented dialog policies via reinforcement learning typically requires large amounts of interaction with users, which in practice renders such methods unusable for re…
Lifelong Inverse Reinforcement Learning
Jorge A. Mendez, Shashank Shivkumar, Eric Eaton
Methods for learning from demonstration (LfD) have shown success in acquiring behavior policies by imitating a user. However, even for a single task, LfD may require numerous demon…
Modular Lifelong Reinforcement Learning via Neural Composition
Jorge A. Mendez, Harm van Seijen, Eric Eaton
Humans commonly solve complex problems by decomposing them into easier subproblems and then combining the subproblem solutions. This type of compositional reasoning permits reuse o…