Learning by Playing - Solving Sparse Reward Tasks from Scratch
Abstract
We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm in the context of Reinforcement Learning (RL). SAC-X enables learning of complex behaviors - from scratch - in the presence of multiple sparse reward signals. To this end, the agent is equipped with a set of general auxiliary tasks, that it attempts to learn simultaneously via off-policy RL. The key idea behind our method is that active (learned) scheduling and execution of auxiliary policies allows the agent to efficiently explore its environment - enabling it to excel at sparse reward RL. Our experiments in several challenging robotic manipulation settings demonstrate the power of our approach.
Keywords
Cite
@article{arxiv.1802.10567,
title = {Learning by Playing - Solving Sparse Reward Tasks from Scratch},
author = {Martin Riedmiller and Roland Hafner and Thomas Lampe and Michael Neunert and Jonas Degrave and Tom Van de Wiele and Volodymyr Mnih and Nicolas Heess and Jost Tobias Springenberg},
journal= {arXiv preprint arXiv:1802.10567},
year = {2018}
}
Comments
A video of the rich set of learned behaviours can be found at https://youtu.be/mPKyvocNe_M