Action and Perception as Divergence Minimization (2009.01791v3)

Abstract: To learn directed behaviors in complex environments, intelligent agents need to optimize objective functions. Various objectives are known for designing artificial agents, including task rewards and intrinsic motivation. However, it is unclear how the known objectives relate to each other, which objectives remain yet to be discovered, and which objectives better describe the behavior of humans. We introduce the Action Perception Divergence (APD), an approach for categorizing the space of possible objective functions for embodied agents. We show a spectrum that reaches from narrow to general objectives. While the narrow objectives correspond to domain-specific rewards as typical in reinforcement learning, the general objectives maximize information with the environment through latent variable models of input sequences. Intuitively, these agents use perception to align their beliefs with the world and use actions to align the world with their beliefs. They infer representations that are informative of past inputs, explore future inputs that are informative of their representations, and select actions or skills that maximally influence future inputs. This explains a wide range of unsupervised objectives from a single principle, including representation learning, information gain, empowerment, and skill discovery. Our findings suggest leveraging powerful world models for unsupervised exploration as a path toward highly adaptive agents that seek out large niches in their environments, rendering task rewards optional.

• The paper introduces a unified framework based on KL divergence minimization to integrate action and perception in intelligent agents.
• The framework leverages informative latent representations and information gain to optimize adaptive behaviors in complex environments.
• The study offers insights into designing agents capable of exploring broader ecological niches through unsupervised learning principles.

An Expert Review of "Action and Perception as Divergence Minimization"

The paper "Action and Perception as Divergence Minimization" proposes a unified framework for intelligent agents to optimize directed behaviors in complex environments. This framework leverages KL divergence minimization, a principle widely used in variational inference and control as inference, to jointly address action and perception processes within agents. By combining these processes, the paper presents an extensive spectrum of objectives, facilitating a cohesive understanding of various agent goals, ranging from narrow domain-specific rewards typical in reinforcement learning, to broader objectives that maximize information and adaptability through latent variable models.

Framework Overview

The authors introduce the Action Perception Divergence (APD), a conceptual tool used to categorize and map the potential space of objective functions for embodied agents. Through APD, agents utilize perception to synchronize their beliefs with reality, while their actions aim to shape the world according to those beliefs. This alignment is achieved by inferring representations to capture past inputs, exploring future inputs that elevate the informativeness of these representations, and selecting actions that significantly influence future inputs.

Key Contributions

1. Unified Framework: The paper positions the minimization of KL divergence as a central framework linking various agent objectives. This unified approach helps in understanding existing objectives more thoroughly and in designing new agent goals.
2. Informative Latent Representations: The authors explore how divergence minimization optimally informs latent representations, influencing both perception and action. This includes traditional representation learning techniques like reconstruction, and exploration strategies such as information gain maximization.
3. Ecological Niches and World Models: The authors suggest that leveraging expressive world models allows agents to inhabit larger ecological niches, which are environments where they can robustly predict inputs and survive external perturbations. This broadens the scope of adaptive agent capabilities, potentially diminishing the necessity for task rewards.

Implications and Future Directions

The introduction of a unified KL divergence framework for action and perception holds substantial implications for both practical applications and theoretical advances in AI. Practically, developing agents that can autonomously explore and adapt to large niches without predefined tasks points towards more generalized and unsupervised learning paradigms. Theoretically, this framework fosters a deeper understanding of how various objectives interrelate, offering a systematic approach for formulating novel AI strategies.

Future research might explore alternative divergence measures, potentially revealing new families of agent objectives with different optimization dynamics and solutions. Empirical investigations into these divergent measures could uncover improved methods for agent training and convergence in complex environments.

Conclusion

The paper "Action and Perception as Divergence Minimization" bridges critical gaps between diverse agent objectives, offering a unified perspective through the lens of KL divergence minimization. By seamlessly integrating action and perception, this framework provides a solid foundation for both existing and yet-to-be-discovered objectives, advancing the discourse on adaptive agent behaviors and world model utilization. As AI continues to evolve, this work stands as a significant contribution towards crafting intelligent agents with more adaptive, information-optimized, and environmentally aware learning strategies.