2 research outputs found
MGHRL: Meta Goal-generation for Hierarchical Reinforcement Learning
Most meta reinforcement learning (meta-RL) methods learn to adapt to new
tasks by directly optimizing the parameters of policies over primitive action
space. Such algorithms work well in tasks with relatively slight difference.
However, when the task distribution becomes wider, it would be quite
inefficient to directly learn such a meta-policy. In this paper, we propose a
new meta-RL algorithm called Meta Goal-generation for Hierarchical RL (MGHRL).
Instead of directly generating policies over primitive action space for new
tasks, MGHRL learns to generate high-level meta strategies over subgoals given
past experience and leaves the rest of how to achieve subgoals as independent
RL subtasks. Our empirical results on several challenging simulated robotics
environments show that our method enables more efficient and generalized
meta-learning from past experience.Comment: Accepted to the ICLR 2020 workshop: Beyond tabula rasa in RL
(BeTR-RL