A Simple Open-loop Model-Free Baseline for Reinforcement Learning Locomotion Tasks without Using Complex Models or Computational Resources
What is deep reinforcement learning used for in robotics?
Deep reinforcement learning (DRL) in robotics is used for decision-making tasks, allowing robots to learn from trial and error in complex and uncertain environments. It enables robots to perform tasks such as grasping objects, navigating, and interacting with humans, without explicit programming. DRL combines deep learning and reinforcement learning techniques to process raw sensory inputs and learn optimal actions for achieving specific goals. This enhances the adaptability and autonomy of robotic systems in various applications, including manufacturing, healthcare, and exploration.
How do complex algorithms impact reproducibility in DRL?
Complex algorithms in Deep Reinforcement Learning (DRL) can negatively impact reproducibility. This complexity often requires detailed task design and specific implementation details for optimal performance, making it difficult for researchers to reproduce results consistently. Additionally, complex models may be more sensitive to variations in hyperparameters, codebases, and evaluation metrics, further complicating reproducibility efforts. Simplifying DRL algorithms and promoting transparency in implementation can help address these challenges.
What are simpler baselines proposed for RL tasks?
Simpler baselines proposed for RL tasks include linear function and radial basis functions (RBF) parametrizations, highlighting the fragility of RL. Another approach involves periodic policies for locomotion, integrating rhythmic movements into robotic control. Recent work has focused on using oscillators to manage locomotion tasks in quadruped robots. A model-free open-loop strategy has also been proposed, leveraging prior knowledge and simple oscillators to generate periodic joint motions1.