5 citations · 6 across the 3 of their papers we have counts for
3 papers
Will it Blend? Composing Value Functions in Reinforcement Learning
Benjamin van Niekerk, Steven James, Adam Earle +1
An important property for lifelong-learning agents is the ability to combine existing skills to solve unseen tasks. In general, however, it is unclear how to compose skills in a pr…
Hierarchical Subtask Discovery With Non-Negative Matrix Factorization
Adam C. Earle, Andrew M. Saxe, Benjamin Rosman
Hierarchical reinforcement learning methods offer a powerful means of planning flexible behavior in complicated domains. However, learning an appropriate hierarchical decomposition…
Hierarchy through Composition with Linearly Solvable Markov Decision Processes
Andrew M. Saxe, Adam Earle, Benjamin Rosman
Hierarchical architectures are critical to the scalability of reinforcement learning methods. Current hierarchical frameworks execute actions serially, with macro-actions comprisin…