Deep Adversarial Reinforcement Learning for Object Disentangling
Melvin Laux,Oleg Arenz,Jan Peters,Joni Pajarinen,Melvin Laux,Oleg Arenz,Jan Peters,Joni Pajarinen
Deep learning in combination with improved training techniques and high computational power has led to recent advances in the field of reinforcement learning (RL) and to successful robotic RL applications such as in-hand manipulation. However, most robotic RL relies on a well known initial state distribution. In real-world tasks, this information is however often not available. For example, when d...
