HAC: Learning Multi-Level Hierarchies with Hindsight

Author(s): Sherwin Chen

Originally published on Towards AI.

Hindsight experience replay for multi-level hierarchies

the 4-level HAC agent on the inverted pendulum

We discuss a novel Hierarchical Reinforcement Learning(HRL) framework that can efficiently learn multiple levels of policies in parallel. Experiments shows, this framework, proposed by Andrew Levy et al. at ICLR 2019, can significantly accelerate learning in sparse reward problems, specifically those whose objective is to reach some goal state. Noticeably, this is the first framework that succeeds in learning 3-level hierarchies in parallel in tasks with continuous state and action space. Some experiments done by the authors even demonstrate its capability to harness 4-level hierarchies. This video shows its competence in 2- and… Read the full blog for free on Medium.

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Published via Towards AI