Hierarchical Imitation and Reinforcement Learning (1803.00590v2)

Abstract: We study how to effectively leverage expert feedback to learn sequential decision-making policies. We focus on problems with sparse rewards and long time horizons, which typically pose significant challenges in reinforcement learning. We propose an algorithmic framework, called hierarchical guidance, that leverages the hierarchical structure of the underlying problem to integrate different modes of expert interaction. Our framework can incorporate different combinations of imitation learning (IL) and reinforcement learning (RL) at different levels, leading to dramatic reductions in both expert effort and cost of exploration. Using long-horizon benchmarks, including Montezuma's Revenge, we demonstrate that our approach can learn significantly faster than hierarchical RL, and be significantly more label-efficient than standard IL. We also theoretically analyze labeling cost for certain instantiations of our framework.

Hierarchical Imitation and Reinforcement Learning

The paper "Hierarchical Imitation and Reinforcement Learning" presents an innovative approach to address challenges inherent in reinforcement learning (RL) environments characterized by sparse reward structures and extended time horizons. These features often complicate the learning of efficient decision-making policies due to delayed or infrequent feedback, which can impede traditional RL algorithms from achieving optimal performance.

The authors introduce a novel hierarchical guidance framework that leverages the hierarchical structure inherent in many decision-making tasks. This framework effectively combines imitation learning (IL) and reinforcement learning (RL) at different hierarchical levels to ameliorate the exploration issues commonly observed in RL and reduce the dependency on extensive expert labeling, a typical limitation in IL.

Core Contributions

The central contributions of this paper can be summarized as follows:

1. Hierarchical Guidance Framework: The proposed framework merges IL and RL methodologies to exploit hierarchical decomposition of tasks. It enables the combination of different modes of expert interaction across various levels of the hierarchy, providing flexibility and efficiency in learning complex policies.
2. Improved Learning Efficiency: By employing this framework, the authors demonstrate substantial improvements in learning speed compared to conventional hierarchical RL and notable reductions in labeling costs compared to standard IL. This is particularly validated through empirical evaluations on long-horizon tasks, including benchmarks like Montezuma's Revenge.
3. Theoretical Analysis: The authors provide a theoretical examination of the labeling costs associated with different implementations of the hierarchical guidance framework. This analytical perspective helps in understanding the trade-offs between expert interaction and learning efficiency.

Experimental Validation

The experimental studies focus on long-horizon decision-making tasks that are known to be challenging under sparse reward conditions. The results exhibit that the hierarchical guidance framework not only accelerates the learning process but also enhances the overall sample efficiency. By integrating IL and RL hierarchically, the proposed algorithm achieves superior performance with reduced computational resources and expert efforts when compared to existing methodologies.

Implications and Future Directions

This research contributes significantly to the field of sequential decision-making by offering a practical solution for efficiently leveraging expert feedback. The hierarchical guidance approach can be particularly beneficial in applications where direct expert demonstrations are expensive or infeasible and where exploration costs in RL are prohibitively high.

The implications of this work extend to a range of potential future developments. Further research could explore the application of this framework to more diverse and complex environments or investigate its integration with other advanced RL methodologies. Moreover, understanding the scalability of the proposed framework in multi-agent systems or its adaptation to real-world robotics control tasks could yield substantial advancements.

In conclusion, this paper provides a substantial methodological advancement in the intersection of imitation learning and reinforcement learning by demonstrating how hierarchical structuring can improve learning outcomes in complex, sparse-reward environments. The findings not only enhance current understanding of hierarchical learning systems but also set a foundation for future exploration and development in this domain.