Authors: Aditya Mohan, Amy Zhang, Marius Lindauer
Deep Reinforcement Learning (RL) has significantly advanced in various fields, from playing complex games to controlling robotic systems. However, its application in real-world scenarios still faces numerous challenges, such as poor data efficiency, limited generalization, and a lack of safety guarantees. This work provides a comprehensive overview of how incorporating structural information into RL can address these challenges.
The Need for Structure in RL
Consider a taxi service RL agent that learns to pick up passengers and drop them off at their destinations in a city grid. Learning the entire city’s layout, traffic patterns, and passenger behaviors all at once can be quite overwhelming. Instead, a better way might be to make this problem more palatable for the RL agent by incorporating changes in different parts of the RL pipeline shown below. Incorporating structure amounts to changing one of the blocks in this pipeline based on assumptions/information about the problem we are trying to solve.
![](https://www.automl.org/wp-content/uploads/2024/07/RL-Pipeline-horizontal-new-1-1024x320.png)
The Spectrum of Decomposability
How can we think about the assumptions and additional information about the problem? One way is by understanding how it reduces the complexity of the problem. For our taxi service agent, we could do this in four archetypical ways:
- Latent Decompositions: abstracting the city grid into a smaller set of critical locations that are easier to manage
- Factored Decomposition: Separating the city into different zones, each with its characteristics.
- Relational Decomposition: Model the relationships between traffic patterns and passenger demand at different times of the day.
- Modular Decomposition: Learning separate policies for navigating different zones of the city.
These decompositions lie on a spectrum. On one end are decompositions that assume a single smaller sub-problem is sufficient to solve the whole problem, and on the other end are those that view the problem as a combination of multiple independent sub-problems. This concept is known as the spectrum of decomposability.
![](https://www.automl.org/wp-content/uploads/2024/07/decomposability-rebuttal-1-1024x266.png)
Patterns of Incorporating Structure
Once we understand how to decompose the problem, we can incorporate this structure into our RL learning process. We can think of these methods as Design Patterns. The term comes from Software Engineering and refers to typical solutions to common problems in software design that provide a blueprint that can be customized to solve specific design challenges. Translated to our RL setting, incorporating structure can be seen as applying design patterns to the RL pipeline. We identified seven patterns in the RL literature:
![](https://www.automl.org/wp-content/uploads/2024/07/patterns.png)
Putting it all together
We can break the city into zones using modular decomposition and create separate policies for each. Then, using the abstraction pattern, we can simplify the state space within each zone to key features like major intersections and passenger hotspots. Next, we apply the augmentation pattern to include the time of day as additional information, helping the taxi understand when and where demand is highest. To make learning more efficient, we use the auxiliary optimization pattern to shape the reward function, encouraging the taxi to stay in high-demand areas when it’s not carrying a passenger.
Finally, we incorporate the environment generation pattern to train the taxi in various simulated conditions, ensuring it can handle different traffic scenarios and weather conditions. By combining these decompositions and patterns, we create a robust and efficient RL model that can navigate the complexities of a real-world city grid.
Conclusion
Incorporating structural assumptions through decompositions and patterns isn’t just about making RL models more efficient; it’s about making them smarter and more adaptable to real-world challenges. So, next time you’re working on applying RL to a real-world problem, think about how you can break it down and infuse structure into your learning process. It might just be the key to unlocking better performance and broader applicability.
Link to the paper: https://www.jair.org/index.php/jair/article/view/15703/27028