Intrinsic Motivation in Reinforcement Learning: A Research Agenda for Adaptive Self-Organisation

Problem

The paper addresses a gap in the understanding of how intrinsic rewards can facilitate adaptation and self-organization in artificial neural systems without relying on external objectives. This is particularly relevant in the context of reinforcement learning, where traditional methods often depend on predefined rewards, limiting the system's ability to explore and adapt autonomously. The work is presented as a preprint, indicating that it has not yet undergone peer review.

Method

The author proposes several intrinsic reward mechanisms to drive adaptive behavior in neural systems, including:

• Empowerment: A measure of the control an agent has over its environment.
• Curiosity: Encouraging exploration by rewarding novel experiences.
• Learning Progress: Rewarding agents based on their improvement over time.
• Information Gain: Focusing on the acquisition of new knowledge.
• Unsupervised Skill Discovery: Enabling agents to identify and learn useful skills without external guidance.
• Mutual Information Estimation: Quantifying the information shared between the agent and its environment.
• World Models: Utilizing internal representations of the environment to inform decision-making.

The author outlines experimental directions to validate these mechanisms, including:

• Testing in resource-constrained environments to assess the stability of behavioral attractors.
• Implementing a network of recurrent agents, each receiving intrinsic rewards to promote diverse behaviors.
• Developing a hierarchical world-model agent aimed at fostering exploratory motor competence.

Results

The available text does not report quantitative results.

Limitations

The author identifies several limitations, including:

• Potential failure modes of intrinsic objectives that may lead to insufficient exploration or the emergence of simplistic behaviors.
• The necessity for complementary objectives to enhance the effectiveness of intrinsic rewards.
• The importance of communication, memory, and learning across multiple temporal scales to support complex adaptive behaviors.

Why it matters

This research agenda has significant implications for the field of reinforcement learning, particularly in developing systems that can autonomously adapt and self-organize. By focusing on intrinsic motivation, the work encourages a shift away from externally imposed objectives, potentially leading to more robust and flexible AI systems capable of navigating complex environments. This could pave the way for advancements in autonomous agents, robotics, and other applications where adaptability is crucial.