Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments

Kidziński Ł; Mohanty SP; Ong C; Huang Z; Zhou S; Pechenko A; Stelmaszczyk A; Jarosik P; Pavlov M; Kolesnikov S; Plis S; Chen Z; Zhang Z; Chen J; Shi J; Zheng Z; Yuan C; Lin Z; Michalewski H; Miłoś P; Osiński B; Melnik A; Schilling M; Ritter H; Carroll S; Hicks J; Levine S; Salathé M; Delp S

Forschungsartikel in Sammelband (Konferenz) | Peer reviewed

Zusammenfassung

In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course. Top participants were invited to describe their algorithms. In this work, we present eight solutions that used deep reinforcement learning approaches, based on algorithms such as Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Trust Region Policy Optimization. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each of the eight teams implemented different modifications of the known algorithms.

Details zur Publikation

Herausgeber*innenEscalera, S.; Weimer, M.
BuchtitelThe NIPS 2017 Competition: Building Intelligent Systems
Seitenbereich121-154
VerlagSpringer
ErscheinungsortLong Beach
StatusVeröffentlicht
Veröffentlichungsjahr2018
KonferenzNIPS, Long Beach, Vereinigte Staaten
Stichwörterdeep reinforcement learning; motor control; biologically inspired; locomotion

Autor*innen der Universität Münster

Schilling, Malte
Professur für Praktische Informatik (Prof. Schilling)