KnowPC: Knowledge-Driven Programmatic Reinforcement Learning for Zero-shot Coordination

TL;DR

KnowPC introduces an interpretable, logic-based programmatic approach to zero-shot coordination in cooperative AI, leveraging domain-specific language, environmental knowledge extraction, and reasoning to improve generalization and interpretability over neural network methods.

Contribution

This work presents a novel framework that combines knowledge extraction and reasoning to automatically learn interpretable program policies for zero-shot coordination.

Findings

Achieves improved generalization in zero-shot coordination tasks.

Provides interpretable policies based on logic and knowledge.

Outperforms neural network-based approaches in key metrics.

Abstract

Zero-shot coordination (ZSC) remains a major challenge in the cooperative AI field, which aims to learn an agent to cooperate with an unseen partner in training environments or even novel environments. In recent years, a popular ZSC solution paradigm has been deep reinforcement learning (DRL) combined with advanced self-play or population-based methods to enhance the neural policy's ability to handle unseen partners. Despite some success, these approaches usually rely on black-box neural networks as the policy function. However, neural networks typically lack interpretability and logic, making the learned policies difficult for partners (e.g., humans) to understand and limiting their generalization ability. These shortcomings hinder the application of reinforcement learning methods in diverse cooperative scenarios.We suggest to represent the agent's policy with an interpretable program. Unlike neural networks, programs contain stable logic, but they are non-differentiable and difficult to optimize.To automatically learn such programs, we introduce Knowledge-driven Programmatic reinforcement learning for zero-shot Coordination (KnowPC). We first define a foundational Domain-Specific Language (DSL), including program structures, conditional primitives, and action primitives. A significant challenge is the vast program search space, making it difficult to find high-performing programs efficiently. To address this, KnowPC integrates an extractor and an reasoner. The extractor discovers environmental transition knowledge from multi-agent interaction trajectories, while the reasoner deduces the preconditions of each action primitive based on the transition knowledge.