Deep active inference agents using Monte-Carlo methods (2006.04176v2)

Abstract: Active inference is a Bayesian framework for understanding biological intelligence. The underlying theory brings together perception and action under one single imperative: minimizing free energy. However, despite its theoretical utility in explaining intelligence, computational implementations have been restricted to low-dimensional and idealized situations. In this paper, we present a neural architecture for building deep active inference agents operating in complex, continuous state-spaces using multiple forms of Monte-Carlo (MC) sampling. For this, we introduce a number of techniques, novel to active inference. These include: i) selecting free-energy-optimal policies via MC tree search, ii) approximating this optimal policy distribution via a feed-forward `habitual' network, iii) predicting future parameter belief updates using MC dropouts and, finally, iv) optimizing state transition precision (a high-end form of attention). Our approach enables agents to learn environmental dynamics efficiently, while maintaining task performance, in relation to reward-based counterparts. We illustrate this in a new toy environment, based on the dSprites data-set, and demonstrate that active inference agents automatically create disentangled representations that are apt for modeling state transitions. In a more complex Animal-AI environment, our agents (using the same neural architecture) are able to simulate future state transitions and actions (i.e., plan), to evince reward-directed navigation - despite temporary suspension of visual input. These results show that deep active inference - equipped with MC methods - provides a flexible framework to develop biologically-inspired intelligent agents, with applications in both machine learning and cognitive science.

• The paper proposes a novel neural architecture that integrates Monte-Carlo methods to select free-energy-optimal policies in high-dimensional spaces.
• The paper introduces a habitual network and Monte-Carlo Dropouts to efficiently mimic cognitive processes and handle model uncertainty.
• The paper demonstrates improved task performance and state transition precision, offering actionable insights for biologically inspired AI systems.

Deep Active Inference Agents Using Monte-Carlo Methods

The paper presents a noteworthy advancement in the field of artificial intelligence and cognitive science by proposing a neural architecture that facilitates the deployment of deep active inference agents in complex, continuous state-space environments. The approach leverages various Monte-Carlo (MC) methods, enhancing the existing framework of active inference to accommodate high-dimensional tasks, thereby addressing a notable bottleneck in the computational implementation of the free-energy principle.

Core Contributions

This paper introduces several novel strategies to the active inference paradigm:

1. Monte-Carlo Tree Search (MCTS): Utilized for selecting free-energy-optimal policies, thereby providing a robust mechanism for planning that aligns with cognitive models of biological agents.
2. Habitual Network: A feed-forward neural network approximates the optimal policy distribution. This network serves to emulate habit formation, reducing computational overhead in familiar task environments.
3. Monte-Carlo Dropouts: Used to predict future parameter belief updates, thereby integrating model uncertainty directly into the inference process.
4. State Transition Precision: A mechanism is designed analogous to attention systems in biological organisms, optimizing state transition precision and thereby enhancing learning efficiency.

Numerical Insights

The implementation demonstrates proficient task performance and environmental dynamics learning, showing superior performance compared to reward-maximizing counterparts in a dynamic 3D environment, Animal-AI, and a simplified 2D setup based on the dSprites dataset. Notably, the agents formed disentangled representations within the latent state space, beneficial for modeling state transitions.

Theoretical and Practical Implications

Deep active inference agents provide a compelling framework for exploring biologically plausible intelligence paradigms, offering potential for cross-disciplinary applications in machine learning and cognitive neuroscience. By seamlessly integrating planning, habitual behavior, and uncertainty management through MC methods, the proposed architecture not only bolsters the theoretical foundation of active inference but also opens new horizons for AI systems emulating complex cognitive functions.

Future inquiries could explore scaling these methodologies to even more complex domains, including the incorporation of episodic memory or extended hierarchical structures for intricate task processing. Furthermore, benchmarking against state-of-the-art reinforcement learning algorithms is essential to delineate active inference's potential advantages or limitations in various scenarios.

Conclusion

The paper reinforces the feasibility and versatility of scaling active inference through advanced neural architectures and MC methods. This convergence of neuroscience and AI not only supports the embodied cognition hypothesis but also contributes a scalable and flexible tool for developing intelligent systems with robust and adaptive capabilities.