Modular Lifelong Reinforcement Learning via Neural Composition (2207.00429v1)

Abstract: Humans commonly solve complex problems by decomposing them into easier subproblems and then combining the subproblem solutions. This type of compositional reasoning permits reuse of the subproblem solutions when tackling future tasks that share part of the underlying compositional structure. In a continual or lifelong reinforcement learning (RL) setting, this ability to decompose knowledge into reusable components would enable agents to quickly learn new RL tasks by leveraging accumulated compositional structures. We explore a particular form of composition based on neural modules and present a set of RL problems that intuitively admit compositional solutions. Empirically, we demonstrate that neural composition indeed captures the underlying structure of this space of problems. We further propose a compositional lifelong RL method that leverages accumulated neural components to accelerate the learning of future tasks while retaining performance on previous tasks via off-line RL over replayed experiences.

The paper "Modular Lifelong Reinforcement Learning via Neural Composition" addresses the challenge of how to enable reinforcement learning (RL) agents to efficiently learn new tasks by leveraging previously acquired knowledge. The key idea is to draw inspiration from human problem-solving strategies, where complex problems are decomposed into simpler subproblems whose solutions can be reused for future tasks with similar underlying structures.

Key Concepts

1. Neural Composition: The primary focus is on a form of composition based on neural modules. By using neural networks, the authors propose to break down tasks into smaller, reusable components or modules. These modules can be combined in various ways to solve different tasks, allowing the agent to generalize more effectively across tasks.
2. Continual/Lifelong Reinforcement Learning: This setting involves a sequence of tasks in which the agent continuously accrues knowledge. The advantage of modular neural composition is that it allows the agent to utilize accumulated components for future tasks, accelerating learning and performance.
3. Compositionality: The problems tackled in this framework intuitively admit compositional solutions, meaning they can be naturally decomposed into subproblems. The paper emphasizes that capturing this compositional structure is crucial for efficient learning and generalization.

Empirical Validation

The authors empirically validate their approach through experiments demonstrating that neural composition can indeed capture the underlying structure of a range of RL problems. Through these experiments, they show that their method allows agents to:

• Accelerate Learning: By leveraging pre-existing neural components, agents can learn new tasks more quickly.
• Retain Performance: The method employs off-line RL over replayed experiences to ensure that the agent retains high performance on previously learned tasks, addressing the common issue of catastrophic forgetting in lifelong learning settings.

Contribution

The paper makes several significant contributions to the field of RL:

• It introduces a novel approach to task decomposition using neural modules.
• It provides a framework for compositional lifelong RL that highlights the importance of reusing prior knowledge.
• It offers empirical evidence supporting the efficacy of neural composition, demonstrating substantial improvements in both learning speed and the retention of learned tasks.

The paper emphasizes that modular approaches not only facilitate better generalization and transfer across tasks but also promise more scalable and efficient lifelong learning systems. The insights and methods presented in this paper could pave the way for more robust and adaptable RL agents capable of handling increasingly complex, real-world environments.