Core Features

KAN-Powered Reinforcement Learning (RL):

• Agent-Environment Interaction: KAN Systems enables agents to learn optimal strategies through continuous interactions with their environments, whether virtual or real-world.

• Advanced Architectures: By replacing traditional Multi-Layer Perceptrons (MLPs) in RL algorithms such as Deep Q-Networks (DQN) and Double Deep Q-Networks (DDQN), KANs provide superior non-linear modeling and policy optimization capabilities.

Enhanced Interpretability:

• Unlike black-box neural networks, KAN Systems allows symbolic extraction of learned policies, offering users a clear, human-readable understanding of how AI agents make decisions. This feature is critical for industries where explainability is key, such as healthcare, autonomous systems, and scientific modeling.

Efficiency with Fewer Parameters:

• KANs utilize learnable activation functions on edges, reducing the number of trainable parameters while maintaining high accuracy. This makes KAN Systems more resource-efficient and scalable for complex RL tasks.

Interpretable Policies via Symbolic Regression:

• The framework provides tools to transfer knowledge from pre-trained RL models into KANs, enabling the extraction of interpretable policies through symbolic regression. These policies can be expressed in simple mathematical forms, making AI agents not only effective but also understandable.

Dynamic Adaptability:

• KAN Systems supports a wide range of RL tasks, from simple environments like CartPole to complex simulations and real-world scenarios. Its flexibility ensures applicability across diverse domains.