Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement Learning

1 code implementation • NeurIPS 2021 • Christopher Hoang, Sungryull Sohn, Jongwook Choi, Wilka Carvalho, Honglak Lee

SFL leverages the ability of successor features (SF) to capture transition dynamics, using it to drive exploration by estimating state-novelty and to enable high-level planning by abstracting the state-space as a non-parametric landmark-based graph.

Efficient Exploration reinforcement-learning +1