Accelerating Reinforcement Learning through Implicit Imitation (1106.0681v1)

Abstract: Imitation can be viewed as a means of enhancing learning in multiagent environments. It augments an agent's ability to learn useful behaviors by making intelligent use of the knowledge implicit in behaviors demonstrated by cooperative teachers or other more experienced agents. We propose and study a formal model of implicit imitation that can accelerate reinforcement learning dramatically in certain cases. Roughly, by observing a mentor, a reinforcement-learning agent can extract information about its own capabilities in, and the relative value of, unvisited parts of the state space. We study two specific instantiations of this model, one in which the learning agent and the mentor have identical abilities, and one designed to deal with agents and mentors with different action sets. We illustrate the benefits of implicit imitation by integrating it with prioritized sweeping, and demonstrating improved performance and convergence through observation of single and multiple mentors. Though we make some stringent assumptions regarding observability and possible interactions, we briefly comment on extensions of the model that relax these restricitions.

Implicit Iitation and Its Role in Accelerating Reinforcement Learning

The paper "Accelerating Reinforcement Learning through Implicit Imitation" by Bob Price and Craig Boutilier introduces the concept of implicit imitation within the field of reinforcement learning (RL). The emphasis of this work is on leveraging observed behaviors of expert agents, termed as mentors, to expedite the learning process of reinforcement learning agents in multiagent environments. Implicit imitation, as elucidated in this paper, is an innovative approach that does not necessitate direct action replication but instead extracts and incorporates transition model information gleaned from a mentor's observed actions under a shared or analogously mapped state space.

Core Contributions

The authors propose a formal model that applies implicit imitation to RL, positing that observations of a mentor can significantly alleviate the exploration burdens typically associated with RL:

1. Model Extraction: The primary mechanism whereby an RL agent, through observation of a mentor's state transitions, forms an inferred mentor action model. This process enables the learner to update its understanding of the state space without needing to navigate it independently.
2. Augmented BeLLMan Backups: By incorporating mentor observations, a reinforcement learner can utilize augmented BeLLMan backups, where the additional information provided by the mentor's experiences contributes to a more precise value estimation.
3. Homogeneous and Heterogeneous Settings: The model accommodates both settings where the observer and mentor have identical action capabilities (homogeneous) and those where they differ (heterogeneous). For heterogeneous settings, feasibility testing and k-step repair strategies are introduced to address the challenges of mismatched action sets, aiming to prevent the RL agent from being misled by unattainable values predicted from the mentor's policy.
4. Confidence Measures and Focusing Techniques: The proposed algorithms include confidence testing to determine the reliability of incorporating mentor data relative to the observer's existing data. Furthermore, focusing computational resources on parts of the state space highlighted by observed mentor behavior is suggested to guide computational effort towards high-value regions.

Empirical Evidence

The authors provide empirical demonstrations to underscore the efficacy of implicit imitation, emphasizing scenarios where traditional RL alone might flounder due to the sheer size of the state space or the qualitative difficulty of the task. These experiments further explore how implicit imitation performs under variations in domain scale and stochasticity and demonstrate the approach's robustness against misleading priors.

Theoretical Analysis and Applicability

The authors engage in a theoretical discussion regarding the applicability of the implicit imitation framework. Factors such as region connectivity, fracture metrics, and relevance to real-world tasks are explored to predict performance and potential suboptimality. The results suggest that implicit imitation can enhance convergence rates significantly in domains where the mentor's and observer's tasks align, even under distinct reward functions or heterogeneous action capabilities.

Extensions and Future Work

The paper proposes several promising extensions to the implicit imitation model. These include integration within partially observable environments, adaptation to continuous and large scale state spaces, and refinement through the incorporation of Bayesian exploration techniques. The inspiration to extend the model to more interactive multi-agent contexts, where agent actions affect each other in a nontrivial manner, represents a fertile ground for future research.

Conclusion

Implicit imitation is positioned as a potent tool for accelerating reinforcement learning, offering a substantial augmentation to conventional exploration strategies, especially in complex or expansive domains. By assimilating mentor-derived insights into the RL agent's learning processes, Price and Boutilier furnish the community with a versatile framework capable of enhancing RL efficiency while adapting to a broad spectrum of multiagent scenarios.