Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey (2003.04960v2)

Abstract: Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback. Despite many advances over the past three decades, learning in many domains still requires a large amount of interaction with the environment, which can be prohibitively expensive in realistic scenarios. To address this problem, transfer learning has been applied to reinforcement learning such that experience gained in one task can be leveraged when starting to learn the next, harder task. More recently, several lines of research have explored how tasks, or data samples themselves, can be sequenced into a curriculum for the purpose of learning a problem that may otherwise be too difficult to learn from scratch. In this article, we present a framework for curriculum learning (CL) in reinforcement learning, and use it to survey and classify existing CL methods in terms of their assumptions, capabilities, and goals. Finally, we use our framework to find open problems and suggest directions for future RL curriculum learning research.

Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey

The paper "Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey" provides an exhaustive examination of curriculum learning (CL) from the perspective of reinforcement learning (RL). The authors have meticulously structured the discussion around the construction and utilization of curricula in RL settings, drawing insights from an assortment of methodologies emerging from recent research. Below, we will explore the key components of curriculum learning as presented in the paper, the implications of these methodologies, and potential avenues for future exploration.

Overview of Curriculum Learning

Curriculum learning is posited as a means to overcome the challenges faced in reinforcement learning regarding the large amounts of interaction that agents typically require with their environment. The key idea is to engage transfer learning by organizing tasks or data samples into a curriculum — an ordered sequence that leverages previously learned knowledge to facilitate learning in subsequent, more complex tasks. This survey distills the various approaches into a coherent framework that catalogs different methods of curriculum generation and sequencing, effectively serving as a guideline for leveraging CL in RL domains.

Methodological Structure

The authors have categorized curriculum learning methodologies into three principal components:

1. Task Generation: This concerns the creation of intermediate tasks, which can be automatically generated or crafted by domain experts. The generation of suitable tasks is crucial as it determines the potential improvement gains offered by the curriculum.
2. Sequencing: This involves ordering tasks or samples to maximize the learning efficiency. The methods discussed vary from sample sequencing within a single task to generating automated curricula through co-learning scenarios, reward modifications, and more.
3. Transfer Learning: Transfer learning is the linchpin that allows knowledge to be carried over across tasks. The paper reviews several mechanisms, including policy transfer, value function transfer, and more abstract partial policy transfer methods.

Key Insights and Challenges

The paper highlights strong numerical results affirming that curricula can improve sample efficiency and learning speed in numerous domains, including toy environments, robotics, and video games. The implications are substantial, suggesting that curriculum learning can be an influential tool in advancing RL applications to handle more complex and realistic tasks. However, the survey also identifies a set of challenges and open problems:

• Task Definition: Fully automating the task generation process without human intervention remains a significant challenge.
• Transfer Mechanisms: Determining how different types of knowledge (e.g., policies, models) can be optimally transferred between tasks to maximize learning outcomes.
• Evaluation Metrics: Distinguishing effective curricula requires robust evaluation metrics. While several metrics have been proposed, refining these so they are universally applicable across diverse domains remains an area for further research.

Theoretical and Practical Implications

Theoretically, the paper prompts us to consider more deeply what constitutes an effective curriculum and how such curricula can be generalized beyond specific tasks to multiple agents or target applications. Practically, the application of curricula could lead to RL agents that learn more robust and generalizable behaviors with less training data, presenting an appealing prospect for real-world deployment.

Future Directions

As the authors conclude, future developments in AI might very well hinge on mastering curriculum generation that is both automated and optimized for complex task domains. The integration of theoretical insights with robust empirical methods will be vital in overcoming existing limitations in curriculum learning and unlocking its full potential.

In synthesis, this paper stands as a comprehensive compendium for researchers aiming to implement or extend curriculum learning in their reinforcement learning paradigms, providing both foundational insights and thought-provoking questions for continued exploration.

The above essay synthesizes the framework, methodologies, and implications of curriculum learning as presented in the paper, effectively consolidating extensive research into a succinct format for experienced researchers.