Cascade Attribute Network: Decomposing Reinforcement Learning Control Policies using Hierarchical Neural Networks
This addresses the problem of policy reusability in practical automation applications, though it appears incremental as it builds on existing hierarchical neural network approaches.
The paper tackles the challenge of reusing pretrained reinforcement learning policies across similar automation tasks by proposing a cascade attribute network (CAN) that decomposes control policies into hierarchical attribute modules. The method achieved zero-shot ideal control policies for multi-attribute robot tasks by directly assembling these modules.
Reinforcement learning methods have been developed to achieve great success in training control policies in various automation tasks. However, a main challenge of the wider application of reinforcement learning in practical automation is that the training process is hard and the pretrained policy networks are hardly reusable in other similar cases. To address this problem, we propose the cascade attribute network (CAN), which utilizes its hierarchical structure to decompose a complicated control policy in terms of the requirement constraints, which we call attributes, encoded in the control tasks. We validated the effectiveness of our proposed method on two robot control scenarios with various add-on attributes. For some control tasks with more than one add-on attribute attribute, by directly assembling the attribute modules in cascade, the CAN can provide ideal control policies in a zero-shot manner.