Program Machine Policy: Addressing Long-Horizon Tasks by Integrating Program Synthesis and State Machines

Program Machine Policy: Addressing Long-Horizon Tasks by Integrating Program Synthesis and State Machines

The paper "Program Machine Policy: Addressing Long-Horizon Tasks by Integrating Program Synthesis and State Machines" introduces an innovative methodology—Program Machine Policy (POMP)—that amalgamates the interpretability of programmatic RL with the inductive generalizability of state machine policies to efficiently solve long-horizon reinforcement learning tasks.

Research Context and Objectives

Deep reinforcement learning (deep RL) has demonstrated remarkable performance in diverse domains, including robotics, strategic games, and video games. Nonetheless, deep RL faces significant challenges in terms of generalizability and interpretability. In contrast, programmatic RL methods focus on synthesizing explicit and human-readable programs that delineate task-solving procedures, thus enhancing interpretability and zero-shot generalizability. However, most programmatic RL techniques are constrained to short-horizon tasks, typically involving fewer than 400 time steps. On the other hand, using state machines to represent RL policies can achieve inductive generalization suitable for long-horizon tasks but struggles with scaling to handle diverse and intricate behaviors.

The objective of this research is to bridge these gaps by proposing the POMP framework. POMP leverages both program synthesis and state machine policies to represent complex behaviors and manage long-horizon tasks efficiently. This work delivers a three-stage framework to retrieve effective, diverse, and compatible programs, which are subsequently used as modes in a state machine. The transition function among these modes is then learned through RL to capture repetitive behaviors and optimize task performance.

Methodology

Constructing Program Embedding Space

To develop a program embedding space that smoothly and continuously parameterizes programs with diverse behaviors, the method utilizes techniques from prior research to train a Variational Autoencoder (VAE). This embedding space is essential for representing programs as points in a latent space, thereby facilitating effective program search and optimization.

Retrieving Mode Programs

The core innovation is the integration of a modified Cross-Entropy Method (CEM) with a diversity multiplier and compatibility evaluation. This hybrid approach ensures that the retrieved program set is not only effective but also behaviorally diverse and mutually compatible. The compatibility component is critical for sequential program execution, crucial for long-horizon task performance.

Learning Transition Function

Given the retrieved set of mode programs, the framework learns a transition function using RL. This transition function determines the probability of transitioning between modes based on the current environment state, optimizing the overall task reward. This hierarchical structure mimics high-level policies managing low-level programmatic actions, akin to the options framework in hierarchical RL.

Evaluation

The effectiveness of POMP is validated through extensive experiments on the Karel domain, encompassing a range of tasks from the Karel, Karel-Hard, and newly introduced Karel-Long problem sets. The results are noteworthy:

• Performance: On Karel and Karel-Hard tasks, POMP surpassed programmatic RL baselines like LEAPS and HPRL in eight out of ten tasks.
• Long-horizon Tasks: On Karel-Long tasks, designed to evaluate the ability to handle tasks requiring thousands of steps, POMP demonstrated superior performance across all tasks compared to deep RL and other programmatic RL baselines.
• Inductive Generalization: POMP showed superior inductive generalization, maintaining high performance even when tested in environments with significantly extended horizons.

Implications and Future Work

The POMP framework represents a meaningful step toward creating RL policies that are both interpretable and capable of generalizing well to long-horizon tasks. The integration of program synthesis with state machines offers a promising approach to addressing the limitations of current deep RL and programmatic RL methods.

Practically, this innovation can lead to more reliable and understandable RL systems suitable for real-world applications, where transparency and adaptability are often critical. Theoretically, this research opens avenues for further exploration in hierarchical policy representations and the development of more advanced program synthesis techniques.

Future work may focus on extending the POMP framework to more complex and diverse domains beyond Karel, incorporating additional domain-specific knowledge into the DSLs used, and exploring more sophisticated methods for learning transition functions to further enhance interpretability and performance.

Conclusion

The "Program Machine Policy" paper presents a compelling approach that bridges the strengths of programmatic RL and state machine policies to solve long-horizon tasks in reinforcement learning. By innovatively combining program synthesis, diversity and compatibility analysis, and state machine transition learning, the POMP framework establishes a new benchmark for interpretability and generalizability in RL, paving the way for future advancements in the field.