InnateCoder: Learning Programmatic Options with Foundation Models

Abstract: Outside of transfer learning settings, reinforcement learning agents start their learning process from a clean slate. As a result, such agents have to go through a slow process to learn even the most obvious skills required to solve a problem. In this paper, we present InnateCoder, a system that leverages human knowledge encoded in foundation models to provide programmatic policies that encode "innate skills" in the form of temporally extended actions, or options. In contrast to existing approaches to learning options, InnateCoder learns them from the general human knowledge encoded in foundation models in a zero-shot setting, and not from the knowledge the agent gains by interacting with the environment. Then, InnateCoder searches for a programmatic policy by combining the programs encoding these options into larger and more complex programs. We hypothesized that InnateCoder's way of learning and using options could improve the sampling efficiency of current methods for learning programmatic policies. Empirical results in MicroRTS and Karel the Robot support our hypothesis, since they show that InnateCoder is more sample efficient than versions of the system that do not use options or learn them from experience.

Analyzing "InnateCoder: Learning Programmatic Options with Foundation Models"

The paper titled "InnateCoder: Learning Programmatic Options with Foundation Models" introduces a novel approach to reinforcement learning (RL) by leveraging foundation models to acquire pre-defined programmatic options. This framework is poised to improve the efficiency of RL agents, reducing the cumbersome and time-intensive process typically associated with tabula rasa learning in traditional RL paradigms.

Key Contributions and Concepts

The core innovation in InnateCoder is the use of foundation models to harness general human knowledge and encode it into "options"—programmatic policies that represent innate skills. These options are obtained in a zero-shot setting, meaning they are derived without prior interaction with the environment. This contrasts with conventional methods that rely on experience gained through exploration, which is often costly in terms of both time and resources.

1. Programmatic Options: InnateCoder breaks down programs generated by foundation models into sub-programs, which serve as temporally extended actions (or options) within RL. This program decomposition into reusable components allows RL agents to utilize sophisticated skills directly from the outset.

2. Semantic and Syntax Spaces: The paper distinguishes between syntax spaces—generated by the context-free grammar of the DSL—and semantic spaces—where programs differ semantically rather than syntactically. InnateCoder constructs semantic spaces based on the behavior of the options, facilitating an efficient search for optimal policies.

3. Global Search Strategy: InnateCoder employs a mixed search strategy that leverages both syntax and semantic spaces. By adjusting the probability of exploring each space, InnateCoder can ensure comprehensive exploration while efficiently targeting promising solutions.

Empirical Evaluation

The study demonstrates the efficacy of InnateCoder through experiments conducted on two distinct domains: MicroRTS (a real-time strategy game) and Karel the Robot (a program synthesis benchmark). The results reveal that InnateCoder surpasses traditional state-of-the-art techniques in terms of sampling efficiency. Specifically:

• MicroRTS: InnateCoder, using options from both GPT-4o and LLaMA 3.1 models, demonstrates superior sample efficiency compared to baselines and competition-winning agents. It shows enhanced performance on maps of varying sizes and complexities.

• Karel the Robot: Across several tasks, InnateCoder not only achieves higher episodic returns but does so more efficiently than methods like Cross Entropy Method (CEM) and stochastic hill-climbing in syntax spaces.

Implications for AI and RL

InnateCoder’s approach opens new possibilities for RL systems by incorporating domain knowledge encoded in large-scale models, thus extending beyond traditional learning paradigms. This facilitates:
- Increased Accessibility: The zero-shot option learning enables small-scale labs to utilize state-of-the-art models without extensive computational resources.
- Enhanced Transferability: Programmatic options derived from general human knowledge could be adapted to various contexts, highlighting their potential for broad applicability in diverse RL tasks.
- Potential Paradigm Shift: This method may influence future RL research to focus on leveraging pre-trained knowledge bases, rather than purely empirical learning from scratch.

Future Directions

In light of these developments, future research might explore:
- Scaling to Continuous Action Spaces: Extending the option-based approach to handle environments that do not have discrete action spaces.
- Refinement of Foundation Models: Enhancing these models to generate more specialized options and addressing limitations related to the scope and depth of their initial training data.
- Integration with Other Learning Paradigms: Investigating how InnateCoder could integrate with other machine learning paradigms, such as supervised or unsupervised learning, to bolster overall learning effectiveness.

In summary, "InnateCoder: Learning Programmatic Options with Foundation Models" introduces a significant shift in how RL problems can be approached, utilizing human knowledge encoded in foundation models for more efficient and effective learning of programmatic policies.