Less is more -- the Dispatcher/ Executor principle for multi-task Reinforcement Learning (2312.09120v1)

Abstract: Humans instinctively know how to neglect details when it comes to solve complex decision making problems in environments with unforeseeable variations. This abstraction process seems to be a vital property for most biological systems and helps to 'abstract away' unnecessary details and boost generalisation. In this work we introduce the dispatcher/ executor principle for the design of multi-task Reinforcement Learning controllers. It suggests to partition the controller in two entities, one that understands the task (the dispatcher) and one that computes the controls for the specific device (the executor) - and to connect these two by a strongly regularizing communication channel. The core rationale behind this position paper is that changes in structure and design principles can improve generalisation properties and drastically enforce data-efficiency. It is in some sense a 'yes, and ...' response to the current trend of using large neural networks trained on vast amounts of data and bet on emerging generalisation properties. While we agree on the power of scaling - in the sense of Sutton's 'bitter lesson' - we will give some evidence, that considering structure and adding design principles can be a valuable and critical component in particular when data is not abundant and infinite, but is a precious resource.

• The paper presents the Dispatcher/Executor principle, dividing the RL controller into a high-level dispatcher and a specialized executor to improve generalization.
• It shows that structuring communication enables zero-effort transfer and boosts efficiency in robotic manipulation tasks in both simulation and real-world environments.
• The study outlines future directions for integrating large multi-modal models to further enhance task adaptability and performance in complex RL settings.

Introduction to the Dispatcher/Executor Principle in RL

Reinforcement Learning (RL) has made significant strides in single-task applications, but real-world situations often require multi-task adaptability. A key challenge is how to build RL systems that can handle multiple tasks without being overwhelmed by the details of each specific environment or device they need to control.

A New Approach: Dispatcher/Executor Principle

The Dispatcher/Executor (D/E) principle is a novel approach proposed to enhance multi-task RL. The D/E principle involves partitioning the RL controller into two distinct parts:

• The Dispatcher: This module understands the task at hand and is tasked with the high-level decision-making process.
• The Executor: This part is responsible for executing the control signals to the device, based on instructions from the dispatcher.

The unique aspect of the D/E principle is that it restricts communication between the dispatcher and executor to a structured format that promotes compositionality and removes irrelevant details, enhancing the system's ability to generalize across different tasks.

Concrete Implementation and Results

By applying the D/E principle to robotic manipulation tasks, both in simulation and actual robots, researchers have observed significant benefits. One of the key findings was the "zero-effort transfer," where the D/E structure was able to apply learned behavior from one task to others without additional training.

Simulations demonstrated that a controller structured around the D/E principle could learn various lifting tasks more efficiently compared to traditional monolithic neural network structures. Moreover, the D/E structure proved more robust to environmental variations and could adapt to new tasks with considerable ease.

Future Directions

Although the current implementations of the D/E principle involve some engineered features and constraints, future work aims to develop end-to-end learning architectures based on this principle.

The primary goal moving forward is to integrate large multi-modal models into the dispatcher module to enhance its ability to interpret task descriptions and to discover regularized representations that facilitate abstract communication between the dispatcher and executor. This will potentially allow for greater generalization capabilities and more robust task execution.

Conclusion

The dispatcher/executor principle introduces an innovative approach to structuring RL controllers for multitasking efficiency. Empirical evaluations show that this design can drastically enhance the generalisation capabilities and data-efficiency of RL systems. The next steps involve refining the principle through learning algorithms, with the aim of further improving flexibility and performance in complex, multi-task environments.