PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling (2404.13423v2)

Abstract: In this work, we introduce PIPER: Primitive-Informed Preference-based Hierarchical reinforcement learning via Hindsight Relabeling, a novel approach that leverages preference-based learning to learn a reward model, and subsequently uses this reward model to relabel higher-level replay buffers. Since this reward is unaffected by lower primitive behavior, our relabeling-based approach is able to mitigate non-stationarity, which is common in existing hierarchical approaches, and demonstrates impressive performance across a range of challenging sparse-reward tasks. Since obtaining human feedback is typically impractical, we propose to replace the human-in-the-loop approach with our primitive-in-the-loop approach, which generates feedback using sparse rewards provided by the environment. Moreover, in order to prevent infeasible subgoal prediction and avoid degenerate solutions, we propose primitive-informed regularization that conditions higher-level policies to generate feasible subgoals for lower-level policies. We perform extensive experiments to show that PIPER mitigates non-stationarity in hierarchical reinforcement learning and achieves greater than 50$\%$ success rates in challenging, sparse-reward robotic environments, where most other baselines fail to achieve any significant progress.

• The paper introduces PIPER, which combines primitive-informed feedback with preference-based learning to tackle sparse-reward challenges in HRL.
• It employs a hierarchical structure where high-level policies set validated subgoals for low-level primitives, reducing subgoal infeasibility.
• Empirical results in robotic navigation and manipulation tasks demonstrate over 50% success rates, highlighting its practical effectiveness.

An Expert Overview of PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling

The paper presents PIPER, an innovative approach in the domain of hierarchical reinforcement learning (HRL) that promises to address persistent challenges in this field. This work integrates the strength of preference-based learning and hierarchical architecture to enhance the performance of agents in complex, sparse-reward environments. The authors claim significant improvements in addressing reward non-stationarity and infeasible subgoal generation, two major hurdles in HRL applications.

PIPER operates at the intersection of HRL and preference-based learning, utilizing the temporal abstraction benefits of HRL and the sample efficiency of preference-based reward models. The hierarchical setup in PIPER distinguishes between high-level and low-level policies where subgoals are provided by a high-level policy and executed by the primitives of low-level policies. The core novelty of PIPER is its use of Primitive-Informed Learning (PiL) feedback, effectively replacing the need for human-derived preferences by leveraging data within the system. This is paired with hindsight relabeling in order to densify sparse reward feedback, thereby improving sample efficiency.

PIPER's contribution is well-supported by extensive empirical validation across multiple robotic navigation and manipulation scenarios, including maze navigation and tasks in the Franka kitchen environment. Particularly, PIPER demonstrates superior sample efficiency and consistently outperforms baseline models, including traditional hierarchical and flat reinforcement learning models. The achievement of over 50% success rates in several sparse-reward tasks that typically stagnate using standard methods is significant.

Key innovations of PIPER involve:

• PiL Feedback: By integrating PiL feedback, the authors have enabled scalable and less labor-intensive preference learning that accords with the dynamics of the task environment, contrasting conventional human-in-the-loop methods that are often cost-prohibitive and less feasible for real-world applications.
• Hindsight Relabeling: The application of hindsight relabeling addresses sparsity by using achieved goals to provide retroactive reward reshaping, enhancing learning efficiency in settings plagued by sparse feedback.
• Primitive-Informed Regularization: This introduces a regularization technique whereby subgoals are calibrated to fit the capabilities of the lower-level primitives, substantially mitigating subgoal infeasibility—a common pitfall in HRL.

In terms of future directions, the PIPER framework paves the way for new research avenues, notably in exploring more complex environments where purely human-driven reward learning remains impractical. The use of learned rewards, derived from system-based preferences, suggests scalability to larger, more complex real-world environments.

Despite its advantages, the paper also identifies limitations such as the computation of state distances in high-dimensional spaces, an issue familiar within the reinforcement learning community. The authors propose potential resolutions through improved state representations, a promising research trajectory that could further enhance the applicability of PIPER in visually rich domains.

In conclusion, PIPER represents a methodological advancement in HRL, leveraging preference-based reward models to overcome persistent challenges in hierarchical setups. Its robust performance in various test environments attests to its potential as a tool in the reinforcement learning toolkit, particularly for tasks characterized by sparse reward landscapes. The results and methodologies presented have implications for both practical implementations in robotics and theoretical advancements in machine learning, emphasizing the value of combining hierarchical structures with preference-based learning mechanisms. As AI systems increasingly encounter complex tasks and environments, innovations like PIPER that enhance learning efficiency and adaptability will be crucial for continued progress.