A Simple Open-loop Model-Free Baseline for Reinforcement Learning Locomotion Tasks without Using Complex Models or Computational Resources
What is deep reinforcement learning (DRL) used for in robotics?
Deep reinforcement learning (DRL) is used in robotics for decision-making, control, and navigation tasks. It enables robots to learn from their environment by performing actions and observing the results, allowing them to adapt their behavior to achieve specific goals. DRL has been applied to various robotic applications, including grasping, object manipulation, and autonomous navigation3.
What issues arise from increasing algorithm complexity in DRL?
Increasing algorithm complexity in DRL leads to issues with reproducibility, as the latest algorithms require many implementation details to perform well on different levels. This complexity also necessitates detailed task design in the form of slow reward engineering. Additionally, complex RL algorithms often struggle with simple problems and can be prone to lower performance when faced with sensor noise or failure.
How do simpler parametrizations impact the fragility of RL?
Simpler parametrizations in RL, such as linear functions or radial basis functions (RBF), can highlight the fragility of RL algorithms2. These simpler approaches may struggle to capture complex patterns and relationships in the data, leading to less effective policies and lower overall performance. However, they require fewer computational resources and can serve as a baseline for comparing more complex models.