Sample-Efficient Learning for Industrial Assembly using Qgraph-bounded DDPG
Sabrina Hoppe,Markus Giftthaler,Robert Krug,Marc Toussaint,Sabrina Hoppe,Markus Giftthaler,Robert Krug,Marc Toussaint
Recent progress in deep reinforcement learning has enabled agents to autonomously learn complex control strategies from scratch. Model-free approaches like Deep Deterministic Policy Gradients (DDPG) seem promising for applications with intricate dynamics, such as contact-rich manipulation tasks. However, these methods typically require large amounts of training data or meticulous hyperparameter tu...
