Cascade Attribute Learning Network
This addresses the challenge of attribute transfer in reinforcement learning for control tasks, offering a novel modular approach that could improve efficiency in multi-task settings.
The paper tackles the problem of transferring learned attributes across control tasks in reinforcement learning by proposing the Cascade Attribute Learning Network (CALNet), which models attributes as separate modules and assembles them, enabling zero-shot transfer to unseen tasks with validation on diverse control problems.
We propose the cascade attribute learning network (CALNet), which can learn attributes in a control task separately and assemble them together. Our contribution is twofold: first we propose attribute learning in reinforcement learning (RL). Attributes used to be modeled using constraint functions or terms in the objective function, making it hard to transfer. Attribute learning, on the other hand, models these task properties as modules in the policy network. We also propose using novel cascading compensative networks in the CALNet to learn and assemble attributes. Using the CALNet, one can zero shoot an unseen task by separately learning all its attributes, and assembling the attribute modules. We have validated the capacity of our model on a wide variety of control problems with attributes in time, position, velocity and acceleration phases.