Work

Deep intrinsically motivated continuous actor-critic for efficient robotic visuomotor skill learning

Year: 2019

Type: article

Abstract: In this paper, we present a new intrinsically motivated actor-critic algorithm for learning continuous motor skills directly from raw visual input. Our neural architecture is composed of a critic and an actor network. Both networks receive the hidden representation of a deep convolutional autoencoder which is trained to reconstruct the visual input, while the centre-most hidden representation is also optimized to estimate the state value. Separately, an ensemble of predictive world models generates, based on its learning progress, an intrinsic reward signal which is combined with the extrinsic reward to guide the exploration of the actor-critic learner. Our approach is more data-efficient and inherently more stable than the existing actor-critic methods for continuous control from pixel data. We evaluate our algorithm for the task of learning robotic reaching and grasping skills on a realistic physics simulator and on a humanoid robot. The results show that the control policies learned with our approach can achieve better performance than the compared state-of-the-art and baseline algorithms in both dense-reward and challenging sparse-reward settings.

Source: Paladyn Journal of Behavioral Robotics

DOI: 10.1515/pjbr-2019-0005

Authors: Muhammad Burhan Hafez, Cornelius Weber, Matthias Kerzel, Stefan Wermter

Institution: Universität Hamburg

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,187

Funders: Deutscher Akademischer Austauschdienst, Deutsche Forschungsgemeinschaft