Fokker-Planck Soft Actor-Critic.

Area of Science:

• Robotics and Control Systems
• Machine Learning
• Computational Physics

Background:

• Complex continuous control tasks require expressive and multimodal policies.
• Existing reinforcement learning (RL) algorithms, including Soft Actor-Critic (SAC), often use unimodal or factorized Gaussian policies, limiting flexibility and leading to mode collapse due to reverse KL-based updates.
• Mode collapse restricts the ability of policies to capture diverse behaviors.

Purpose of the Study:

• To develop a principled policy optimization algorithm for stable and accurate learning of multimodal policies.
• To address the limitations of existing RL algorithms in handling multimodal distributions and preventing mode collapse.
• To enhance the representational flexibility of policies in continuous control tasks.

Main Methods:

• Proposing Fokker-Planck Soft Actor-Critic (FP-SAC), a novel reinforcement learning algorithm.
• Formulating soft policy improvement using stochastic differential equations.
• Deriving a distribution-level objective from the Fokker-Planck (FP) equation and minimizing its residual using physics-informed learning.
• Leveraging normalizing flows for enhanced policy expressiveness.

Main Results:

• FP-SAC enables stable and accurate learning of multimodal policies.
• The algorithm effectively captures multimodal behaviors in experiments.
• FP-SAC achieves higher task performance and greater stability compared to existing approaches on multigoal environments, MuJoCo, and Meta-World benchmarks.
• Demonstrated mitigation of mode collapse issues inherent in standard SAC.

Conclusions:

• FP-SAC offers a principled and effective approach to learning multimodal policies in continuous control.
• The method enhances policy representational flexibility and overcomes limitations of traditional RL algorithms.
• FP-SAC shows significant promise for tackling complex control problems requiring diverse behavioral strategies.