A critical assessment of reinforcement learning methods for microswimmer navigation in complex flows

Area of Science:

• Fluid Mechanics
• Robotics
• Artificial Intelligence

Background:

• Planktonic organisms and autonomous robots face challenges navigating fluid flows using only onboard sensors.
• Reinforcement learning (RL) is increasingly used for this, but algorithm performance is often unclear.

Purpose of the Study:

• To quantitatively assess RL methods for navigation in partially observable fluid flows.
• To compare the performance of different RL algorithms against a known quasi-optimal policy.

Main Methods:

• Introduced a directional navigation problem with a known analytical solution.
• Evaluated Q-Learning and Advantage Actor Critic (A2C) against Proximal Policy Optimization (PPO).
• Utilized vectorized environments, generalized advantage estimation, and hyperparameter optimization for PPO.

Main Results:

• Common RL algorithms (Q-Learning, A2C) showed poor performance and robustness in various flows.
• PPO significantly surpassed these methods, matching theoretical quasi-optimal performance in turbulent flow.
• PPO demonstrated robust and near-optimal navigation strategies.

Conclusions:

• Algorithm selection and implementation details are crucial for effective autonomous navigation in complex flows.
• Advanced RL algorithms like PPO are essential for developing sophisticated navigation strategies.
• Fine-tuning and advanced techniques are key to achieving high performance in RL-based navigation.