Language as an Abstraction for Hierarchical Deep Reinforcement Learning (1906.07343v2)

Abstract: Solving complex, temporally-extended tasks is a long-standing problem in reinforcement learning (RL). We hypothesize that one critical element of solving such problems is the notion of compositionality. With the ability to learn concepts and sub-skills that can be composed to solve longer tasks, i.e. hierarchical RL, we can acquire temporally-extended behaviors. However, acquiring effective yet general abstractions for hierarchical RL is remarkably challenging. In this paper, we propose to use language as the abstraction, as it provides unique compositional structure, enabling fast learning and combinatorial generalization, while retaining tremendous flexibility, making it suitable for a variety of problems. Our approach learns an instruction-following low-level policy and a high-level policy that can reuse abstractions across tasks, in essence, permitting agents to reason using structured language. To study compositional task learning, we introduce an open-source object interaction environment built using the MuJoCo physics engine and the CLEVR engine. We find that, using our approach, agents can learn to solve to diverse, temporally-extended tasks such as object sorting and multi-object rearrangement, including from raw pixel observations. Our analysis reveals that the compositional nature of language is critical for learning diverse sub-skills and systematically generalizing to new sub-skills in comparison to non-compositional abstractions that use the same supervision.

Language as an Abstraction for Hierarchical Deep Reinforcement Learning

The paper "Language as an Abstraction for Hierarchical Deep Reinforcement Learning" proposes an innovative framework to tackle the complex problem of solving temporally-extended tasks within reinforcement learning (RL) using hierarchical RL and the compositional nature of language. The central hypothesis of the paper suggests that language, with its inherent compositionality and expressive power, provides an effective abstraction mechanism that can enhance hierarchical RL by enabling fast learning and generalizing to new tasks.

Key Contributions

1. Language as a High-Level Abstraction: The authors propose utilizing language not only as an interface but as a fundamental abstraction to bridge high- and low-level policies. The low-level policy is trained to follow language-based instructions, allowing the high-level policy to generate these instructions as abstract actions. This setup, referred to as Hierarchical Abstraction with Language (HAL), permits agents to leverage structured language to manage complex task dynamics effectively.
2. Compositional Task Learning Environment: The paper introduces a new object interaction environment, implemented using the MuJoCo physics engine and the CLEVR engine, to empirically paper compositional task learning. This environment is designed to challenge agents with tasks such as object sorting and rearrangement, which are naturally decomposable into sub-tasks representable by language instructions.
3. Hindsight Instruction Relabeling (HIR): A novel instructional relabeling strategy, HIR, is proposed to address the sparse reward problem typically encountered in RL tasks. By reassigning goals with language instructions achieved throughout the trajectory retrospectively, the approach provides dense learning signals that improve training efficiency.

Experimental Results

The experiments demonstrate that the proposed HAL framework excels in learning temporally-extended tasks, outperforming traditional hierarchical RL approaches that rely on non-compositional abstractions. Key findings include:

• Agents trained with language abstractions achieved higher task performance compared to models with non-compositional representations and one-hot encodings, highlighting the importance of language's compositional structure.
• The framework successfully scales from state-based scenarios to challenging vision-based environments, maintaining robustness across diverse task sets.
• Agents trained under HAL show promising generalization capabilities, effectively transferring learned skills to new instruction sets with systematic variations—pointing to the potential for combinatorial generalization in hierarchical RL.

Implications and Future Directions

The integration of language as both a tool for communication and a representational abstraction in hierarchical RL paves the way for more human-like reasoning and problem-solving capabilities in autonomous systems. Practically, such approaches can significantly benefit robotics and other domains where interpretability, flexibility, and scaling to complex tasks are primary concerns.

Theoretically, the work expands understanding of how compositional structures like language can interact with learning algorithms to foster systematic generalization. This aligns with broader goals in AI of creating systems able to generalize beyond training distributions.

Future research could explore leveraging state-of-the-art LLMs for higher-level tasks in hierarchical RL, potentially increasing the abstraction’s power. Additionally, further investigation into integrating real-world data, such as pre-trained vision-linguistic models or human-in-the-loop systems, could enhance the applicability of HAL to practical, real-world problems where supervised labeling or reward function crafting is not feasible.

In summary, "Language as an Abstraction for Hierarchical Deep Reinforcement Learning" presents a robust framework that not only enhances hierarchical RL with language's unique compositional capabilities but also introduces directions for substantial advancements in AI research and application.