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Hybrid Inverse Reinforcement Learning (2402.08848v2)

Published 13 Feb 2024 in cs.LG and cs.AI

Abstract: The inverse reinforcement learning approach to imitation learning is a double-edged sword. On the one hand, it can enable learning from a smaller number of expert demonstrations with more robustness to error compounding than behavioral cloning approaches. On the other hand, it requires that the learner repeatedly solve a computationally expensive reinforcement learning (RL) problem. Often, much of this computation is wasted searching over policies very dissimilar to the expert's. In this work, we propose using hybrid RL -- training on a mixture of online and expert data -- to curtail unnecessary exploration. Intuitively, the expert data focuses the learner on good states during training, which reduces the amount of exploration required to compute a strong policy. Notably, such an approach doesn't need the ability to reset the learner to arbitrary states in the environment, a requirement of prior work in efficient inverse RL. More formally, we derive a reduction from inverse RL to expert-competitive RL (rather than globally optimal RL) that allows us to dramatically reduce interaction during the inner policy search loop while maintaining the benefits of the IRL approach. This allows us to derive both model-free and model-based hybrid inverse RL algorithms with strong policy performance guarantees. Empirically, we find that our approaches are significantly more sample efficient than standard inverse RL and several other baselines on a suite of continuous control tasks.

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Enhancing Inverse Reinforcement Learning with Hybrid Approaches for Improved Sample Efficiency

Introduction

Inverse Reinforcement Learning (IRL) represents a powerful framework for learning from demonstrations, particularly useful for complex tasks where specifying an explicit reward function is challenging. This paper introduces a novel approach, termed Hybrid Inverse Reinforcement Learning (Hybrid IRL), aiming to improve sample efficiency by integrating both model-free and model-based elements into IRL. By leveraging expert demonstrations more effectively, Hybrid IRL significantly reduces the need for extensive exploration, a notable limitation in conventional IRL methods.

The Challenge of Exploration in IRL

Traditional Inverse RL methods often suffer from inefficient exploration, requiring significant computational resources to solve the underlying reinforcement learning problems. This inefficiency stems from the necessity of global exploration – the process of searching across all possible states to identify optimal decisions, which is computationally intensive and practically infeasible in complex environments. The proposed Hybrid IRL method addresses this challenge by integrating expert demonstrations directly into the policy search process, thus narrowing the search space and focusing exploration on areas of the state space that are relevant and similar to those encountered by the expert.

The Concept of Hybrid IRL

Hybrid IRL operates on the insight that leveraging a mixture of online interactions and expert data during the policy optimization phase can drastically condense the necessary exploration to compute a strong policy. This approach diverges from traditional methods that either fully rely on online data (online RL) or solely on expert demonstrations (behavioral cloning) and instead proposes a balanced methodology that benefits from the strengths of both. The key contributions of Hybrid IRL include:

  1. Reduction from IRL to Expert-Competitive RL: By guaranteeing the output policy competes with the expert, rather than aiming for global optimality, significant reductions in interaction during policy search are achieved.
  2. Development of Hybrid Algorithms: The paper introduces model-free (HyPE) and model-based (HyPER) hybrid IRL algorithms, offering robust policy performance guarantees under both frameworks.
  3. Empirical Validation: Through experiments on continuous control tasks, the proposed Hybrid IRL methods demonstrate marked improvements in sample efficiency over standard IRL and other baseline methods.

Practical Implications

The hybrid approach offers practical advantages in environments where computational resources are limited or where safety constraints limit the feasibility of extensive exploration. For instance, in robotics, where real-world interactions are costly and potentially hazardous, the ability to learn efficiently from a limited set of expert demonstrations can accelerate the development of autonomous systems. Furthermore, the flexibility of Hybrid IRL, encompassing both model-free and model-based methods, allows for adaptation based on the specific requirements of the task and the availability of a dynamic model of the environment.

Future Directions

While the current work showcases promising results, further exploration is warranted to fully understand the bounds of Hybrid IRL's applicability. For instance, investigating the performance of Hybrid IRL in environments with high-dimensional state spaces or complex dynamics could provide deeper insights into its scalability and robustness. Additionally, refining the theoretical underpinnings to relax certain assumptions, such as the requirement for expert policy realizability, could broaden the method's applicability. Lastly, exploring the integration of Hybrid IRL with other learning paradigms, such as meta-learning or transfer learning, could unveil new avenues for efficient learning in diverse and changing environments.

Conclusion

Hybrid Inverse Reinforcement Learning introduces a novel and efficient strategy for learning from expert demonstrations, effectively addressing the exploration inefficiency prevalent in traditional IRL methods. By thoughtfully merging online and expert data within the learning process, Hybrid IRL not only promises enhanced sample efficiency but also opens new possibilities for learning in complex, real-world tasks where direct exploration is either impractical or impossible. This work lays a solid foundation for future investigations into more adaptable, efficient, and practical approaches to Inverse Reinforcement Learning.

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Authors (5)
  1. Juntao Ren (5 papers)
  2. Gokul Swamy (26 papers)
  3. Zhiwei Steven Wu (143 papers)
  4. J. Andrew Bagnell (64 papers)
  5. Sanjiban Choudhury (62 papers)
Citations (10)
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