Deep Adversarial Reinforcement Learning for Object Disentangling

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 disentangling waste objects the actual position of the robot w. r. t. the objects may not match the positions the RL policy was trained for. To solve this problem, we present a novel adversarial reinforcement learning (ARL) framework. The ARL framework utilizes an adversary, which is trained to steer the original agent, the protagonist, to challenging states. We train the protagonist and the adversary jointly to allow them to adapt to the changing policy of their opponent. We show that our method can generalize from training to test scenarios by training an end-to-end system for robot control to solve a challenging object disentangling task. Experiments with a KUKA LBR+ 7-DOF robot arm show that our approach outperforms the baseline method in disentangling when starting from different initial states than provided during training.

Authors

• Melvin Laux
• Oleg Arenz
• Jan Peters 0001
• Joni Pajarinen

Keywords

• Training
• Visualization
• Sensitivity
• Shape
• Reinforcement learning
• Task analysis
• Robots
• Deep Learning
• Generative Adversarial Networks
• Deep Reinforcement Learning
• Adversarial Reinforcement Learning
• Protagonist
• Learning Framework
• Robotic Arm
• Test Scenarios
• Reinforcement Learning Framework
• High Computational Power
• Reinforcement Learning Policy
• Training Set
• Horizon
• Time Step
• Number Of Steps
• State Space
• Start Position
• Reward Function
• Markov Decision Process
• General Policy
• Reinforcement Learning Methods
• Policy Learning
• Beginning Of Episode
• Deep Reinforcement Learning Method
• State-value Function
• Training Scenarios
• Replay Buffer
• Types Of Episodes
• Adversarial Attacks
• State St