38 citations · 65 across the 3 of their papers we have counts for
3 papers
cs.LG2019★ 38 cited
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
André Barreto, Diana Borsa, John Quan +6
The ability to transfer skills across tasks has the potential to scale up reinforcement learning (RL) agents to environments currently out of reach. Recently, a framework based on…
cs.LG2018★ 24 cited
Universal Successor Features Approximators
Diana Borsa, André Barreto, John Quan +5
The ability of a reinforcement learning (RL) agent to learn about many reward functions at the same time has many potential benefits, such as the decomposition of complex tasks int…
cs.AI2016★ 3 cited
Situational Awareness by Risk-Conscious Skills
Daniel J. Mankowitz, Aviv Tamar, Shie Mannor
Hierarchical Reinforcement Learning has been previously shown to speed up the convergence rate of RL planning algorithms as well as mitigate feature-based model misspecification (M…