Learning by Playing - Solving Sparse Reward Tasks from Scratch

02/28/2018
by   Martin Riedmiller, et al.
0

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.

READ FULL TEXT

page 6

page 7

page 8

page 14

page 16

page 17

page 18

research
10/28/2020

Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in First-person Simulated 3D Environments

First-person object-interaction tasks in high-fidelity, 3D, simulated en...
research
03/19/2018

Automated Curriculum Learning by Rewarding Temporally Rare Events

Reward shaping allows reinforcement learning (RL) agents to accelerate l...
research
06/20/2022

EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RL

Reinforcement learning (RL) in long horizon and sparse reward tasks is n...
research
05/15/2020

Simple Sensor Intentions for Exploration

Modern reinforcement learning algorithms can learn solutions to increasi...
research
06/22/2020

Ecological Reinforcement Learning

Much of the current work on reinforcement learning studies episodic sett...
research
12/24/2021

On the Unreasonable Efficiency of State Space Clustering in Personalization Tasks

In this effort we consider a reinforcement learning (RL) technique for s...
research
11/19/2020

Parrot: Data-Driven Behavioral Priors for Reinforcement Learning

Reinforcement learning provides a general framework for flexible decisio...

Please sign up or login with your details

Forgot password? Click here to reset