Simplifying Reinforcement Learning with Open-Loop Trajectory Generation

In this research paper, we explore the significance of policy structures in deep reinforcement learning (RL) algorithms. Specifically, we examine how adopting specific structural elements can significantly improve the performance of even a minimal policy. Our findings highlight the importance of leveraging prior knowledge to create simpler and more efficient RL algorithms.
To understand this concept, imagine building a car. Just like how a car has a specific structure (chassis, engine, wheels, etc.), RL algorithms also have a set of underlying policies that determine how they learn and make decisions. By using the right structural elements, we can create a more efficient and effective car that can drive smoothly without many complicated parts.
One of the key findings in our research is that open-loop oscillators are much faster than other RL algorithms. They require only a few minutes of CPU time to train on a single environment for one million steps, while other methods like SAC demand a GPU (Graphics Processing Unit) to achieve reasonable runtimes. This means that open-loop oscillators can be easily scaled using asynchronous parallelization to achieve satisfactory performance in a timely manner, even on embedded systems with limited computing resources.
Another important aspect of our research is the need for simplicity in RL algorithms. Many complex methods are being developed, but they often come with high computational requirements and limited scalability. By introducing an extremely simple open-loop trajectory generator that operates independently of sensor data, we aim to demystify the field and highlight the importance of keeping things simple.
In conclusion, our research shows that adopting specific structural elements can significantly improve the performance of RL algorithms, making them more efficient and effective. By leveraging prior knowledge and simplicity, we can create better and faster RL algorithms that can be easily deployed on embedded systems with limited computing resources. This has important implications for a wide range of applications, from robotics to autonomous vehicles, where efficiency and scalability are crucial factors.