Stochastic Neural Networks for Hierarchical Reinforcement Learning (1704.03012v1)

Abstract: Deep reinforcement learning has achieved many impressive results in recent years. However, tasks with sparse rewards or long horizons continue to pose significant challenges. To tackle these important problems, we propose a general framework that first learns useful skills in a pre-training environment, and then leverages the acquired skills for learning faster in downstream tasks. Our approach brings together some of the strengths of intrinsic motivation and hierarchical methods: the learning of useful skill is guided by a single proxy reward, the design of which requires very minimal domain knowledge about the downstream tasks. Then a high-level policy is trained on top of these skills, providing a significant improvement of the exploration and allowing to tackle sparse rewards in the downstream tasks. To efficiently pre-train a large span of skills, we use Stochastic Neural Networks combined with an information-theoretic regularizer. Our experiments show that this combination is effective in learning a wide span of interpretable skills in a sample-efficient way, and can significantly boost the learning performance uniformly across a wide range of downstream tasks.

• The paper’s main contribution is a framework that pretrains diverse skills using SNNs and MI regularization to enhance exploration in hierarchical RL.
• It employs a hierarchical policy where a Manager Network selects high-level skills to efficiently address sparse reward challenges.
• Experimental results in maze navigation and food collection tasks show significant improvements in sample efficiency and learning speed.

Stochastic Neural Networks for Hierarchical Reinforcement Learning

The paper "Stochastic Neural Networks for Hierarchical Reinforcement Learning" proposes a novel framework designed to enhance the performance of deep reinforcement learning (RL) in environments with sparse rewards or extended task horizons. The essence of this research is to integrate hierarchical modeling of actions with intrinsic motivation in a manner that effectively reduces sample complexity and improves task transferability.

Core Contribution and Methodology

The primary contribution of the research is the development of a framework that first learns a set of diverse skills in a pre-training environment and subsequently employs these skills to facilitate exploration in downstream tasks. This is achieved through the utilization of Stochastic Neural Networks (SNNs) enhanced by an information-theoretic regularizer.

In the pre-training phase, the model learns a range of skills using skills-driven proxy rewards that require minimal domain-specific knowledge. These are encoded using SNNs, which inherently support multi-modality, allowing the model to represent and leverage a diverse array of skills effectively.

An integral part of the framework is the application of a Mutual Information (MI) regularization term. This regularizer is used during the pre-training phase to encourage diversity in the learned skills by maximizing the MI between latent variables representing skills and the agent's state observations. The choice of MI is crucial as it aligns with the goal of ensuring that each skill corresponds to a distinct, interpretable behavior.

To incorporate these learned skills into practical application, the framework employs a hierarchical policy structure. Here, a Manager Network is tasked with selecting and executing high-level skills over extended temporal segments, enhancing exploration capabilities in environments characterized by sparse rewards.

Experimental Validation

The effectiveness of the proposed framework is demonstrated through comprehensive experiments involving tasks that pose significant challenges due to the sparsity of rewards. These experiments include a set of maze navigation and food collection tasks that simulate real-world RL problems. The results provide strong numerical support, indicating that the hierarchical use of skills learned by SNNs substantially accelerates learning compared to baseline methods. Moreover, the paper reveals a marked improvement in performance when the information-theoretic regularizer is applied, showcasing the benefit of MI in promoting skill diversity.

Theoretical and Practical Implications

From a theoretical vantage point, the framework extends the applicability of hierarchical reinforcement learning (HRL) by circumventing the need for hand-engineered skill hierarchies. It introduces a mechanism to encode and reuse skills effectively across diverse problems, demonstrating improved sample efficiency and enhanced exploration.

Practically, this research has profound implications for developing autonomous systems that operate in complex and dynamic environments. The capability to learn and exploit a variety of skills can significantly reduce the time and data required to adapt to new tasks, which is crucial in robotics, automated navigation, and other domains requiring adaptable decision-making.

Future Developments

While the proposed framework shows promise, it opens avenues for further research. Future work could explore integrating end-to-end learning mechanisms that dynamically adjust skill selections in response to real-time feedback, enhancing adaptability. Additionally, experimenting with various architectures for SNNs or exploring more sophisticated forms of mutual information regularization could further optimize skill learning and transfer.

In conclusion, the paper contributes to the RL field by proposing a robust method to tackle tasks with sparse rewards and long horizons through a combination of hierarchical policy learning and stochastic neural networks. This work lays a foundation for advancing RL applications in environments where adaptability and efficient learning are paramount.