Chain of Thought Imitation with Procedure Cloning (2205.10816v1)

Abstract: Imitation learning aims to extract high-performance policies from logged demonstrations of expert behavior. It is common to frame imitation learning as a supervised learning problem in which one fits a function approximator to the input-output mapping exhibited by the logged demonstrations (input observations to output actions). While the framing of imitation learning as a supervised input-output learning problem allows for applicability in a wide variety of settings, it is also an overly simplistic view of the problem in situations where the expert demonstrations provide much richer insight into expert behavior. For example, applications such as path navigation, robot manipulation, and strategy games acquire expert demonstrations via planning, search, or some other multi-step algorithm, revealing not just the output action to be imitated but also the procedure for how to determine this action. While these intermediate computations may use tools not available to the agent during inference (e.g., environment simulators), they are nevertheless informative as a way to explain an expert's mapping of state to actions. To properly leverage expert procedure information without relying on the privileged tools the expert may have used to perform the procedure, we propose procedure cloning, which applies supervised sequence prediction to imitate the series of expert computations. This way, procedure cloning learns not only what to do (i.e., the output action), but how and why to do it (i.e., the procedure). Through empirical analysis on navigation, simulated robotic manipulation, and game-playing environments, we show that imitating the intermediate computations of an expert's behavior enables procedure cloning to learn policies exhibiting significant generalization to unseen environment configurations, including those configurations for which running the expert's procedure directly is infeasible.

• The paper introduces Procedure Cloning, which enriches traditional imitation learning by capturing intermediate expert computation steps.
• It employs supervised sequence prediction with autoregressive models to mirror multi-step expert planning and decision-making.
• Empirical results demonstrate that Procedure Cloning outperforms conventional behavioral cloning in tasks like maze navigation, robotic manipulation, and strategic gaming.

Overview of "Chain of Thought Imitation with Procedure Cloning"

The paper "Chain of Thought Imitation with Procedure Cloning" addresses limitations in traditional imitation learning paradigms, proposing an innovative approach termed Procedure Cloning (PC). Traditional imitation learning frames the challenge of learning policies as a supervised learning problem, primarily focusing on mimicking expert behavior through input-output mappings of observed state-action pairs. However, this methodology often fails to generalize effectively beyond the specific scenarios observed during training.

Key Contributions

The authors introduce Procedure Cloning, an advanced imitation learning approach that not only seeks to replicate final actions but also the sequence of intermediate computations performed by experts. This enhances the understanding and emulation of expert reasoning, aiming to improve policy generalization across varied and unseen environments.

• Procedure Observation: The authors propose augmenting the typical state-action data with "procedure observations," capturing intermediate computational steps that lead to an expert's decision. This enriched dataset enables a deeper insight into expert methodologies.
• Supervised Sequence Prediction: A significant innovation of Procedure Cloning lies in its application of supervised sequence prediction, utilizing models akin to autoregressive transformers. This approach is aimed at replicating the thought process of experts, providing a structured framework to learn and predict intermediate procedures preceding decision outputs.

Empirical Analysis and Results

Through rigorous testing across navigation, manipulation, and gaming environments, Procedure Cloning demonstrates substantial enhancements in policy generalization compared to conventional Behavioral Cloning (BC) and its variants.

• Maze Navigation: When tested on both discrete and continuous maze environments, PC significantly outperformed BC, particularly in handling new and complex maze configurations. The success was attributed to the inherent ability of PC to simulate multi-step planning, enhancing adaptability to unseen obstacles and layouts.
• Robotic Manipulation: In intricate robotic tasks, such as bimanual sweeps, PC agents achieved superior generalization by leveraging the procedural steps of expert demonstration. This was reflected in better task completion metrics, highlighting PC's potential in vision-based robotic applications.
• Strategic Games: In the MinAtar testbed, PC exhibited robust performance across stochastic environments and varying game difficulty levels. Notably, the predictive sequence modeling evident in PC enabled the agent to effectively emulate complex game strategies derived from Monte Carlo Tree Search (MCTS) simulations.

Theoretical and Practical Implications

Theoretical implications of this work suggest a paradigm shift in imitation learning by emphasizing the importance of capturing the procedural context underlying expert action. This approach challenges the conventional wisdom of treating learning as a simple mapping problem, encouraging a broader interpretation that includes expert reasoning and methodology.

Practically, Procedure Cloning has notable applications in fields that require adaptive learning from limited data, such as autonomous navigation and robotic manipulation under diverse conditions. By embarking on a deeper understanding of procedural learning, this research sets the stage for future advances in adaptive AI systems capable of broader generalizations.

Speculative Future Developments

As AI continues to integrate into complex, dynamic environments, the methodology presented in this paper could be extended to areas like real-time strategy in highly variable domains. Further, enhancing the scalability and efficiency of PC through optimization of sequence prediction models could democratize its application across even wider practical domains.

To conclude, "Chain of Thought Imitation with Procedure Cloning" presents a compelling case for evolving imitation learning methods to incorporate richer expert insights, paving the way for more intelligent and adaptable autonomous systems.