Learning, Planning, and Control in a Monolithic Neural Event Inference Architecture (1809.07412v2)

Abstract: We introduce REPRISE, a REtrospective and PRospective Inference SchEme, which learns temporal event-predictive models of dynamical systems. REPRISE infers the unobservable contextual event state and accompanying temporal predictive models that best explain the recently encountered sensorimotor experiences retrospectively. Meanwhile, it optimizes upcoming motor activities prospectively in a goal-directed manner. Here, REPRISE is implemented by a recurrent neural network (RNN), which learns temporal forward models of the sensorimotor contingencies generated by different simulated dynamic vehicles. The RNN is augmented with contextual neurons, which enable the encoding of distinct, but related, sensorimotor dynamics as compact event codes. We show that REPRISE concurrently learns to separate and approximate the encountered sensorimotor dynamics: it analyzes sensorimotor error signals adapting both internal contextual neural activities and connection weight values. Moreover, we show that REPRISE can exploit the learned model to induce goal-directed, model-predictive control, that is, approximate active inference: Given a goal state, the system imagines a motor command sequence optimizing it with the prospective objective to minimize the distance to the goal. The RNN activities thus continuously imagine the upcoming future and reflect on the recent past, optimizing the predictive model, the hidden neural state activities, and the upcoming motor activities. As a result, event-predictive neural encodings develop, which allow the invocation of highly effective and adaptive goal-directed sensorimotor control.

Learning, Planning, and Control in a Monolithic Neural Event Inference Architecture

The paper entitled "Learning, Planning, and Control in a Monolithic Neural Event Inference Architecture" introduces a novel framework for inferring event states and controlling dynamical systems using an advanced neural network methodology. The authors present \METHOD*, a retrospective and prospective inference scheme, built around the principles of active inference and predictive coding, implemented via recurrent neural networks (RNNs). This methodology is proposed to enhance the capability of neural networks in adapting to various dynamical environments, particularly for goal-directed tasks without predefined event boundaries or contexts.

Core Contributions

The key contribution of \METHOD* lies in its dual inference mechanism, which retrospectively analyses past experiences to update internal states and prospectively projects actions to achieve future goals. This is operationalized through an RNN supplemented with contextual neurons, facilitating the disentanglement and encoding of complex sensorimotor dynamics into discrete event codes. The system optimizes upcoming motor actions in the context of these inferred event states, thereby enabling a flexible adaptation to dynamic changes in the operational environment.

Methodology and Results

The system's architecture is evaluated in a controlled simulation, wherein it learns to control three types of vehicles based on sensorimotor inputs without explicit information about the identity of the vehicle. The RNN, trained using sensory prediction errors, successfully infers the current vehicle type from the observation of dynamical behavior over time. This ability to infer context states endogenously suggests a robust potential for \METHOD* to model event-oriented cognitive processes akin to human conceptual abstraction.

Empirical results demonstrate the system's efficacy in reaching predefined goal states with an impressive accuracy, maintaining competitive control performance even without explicit context vectors during training. Notably, when context states are not predefined but eventually emerge through learning, \METHOD* exhibits superior goal-directed performance, underscoring its capability for self-organized abstraction of complex sensorimotor patterns.

Theoretical Implications

This research situates itself at the intersection of predictive coding and active inference theories, suggesting an emergent framework where neural networks can autonomously deduce hierarchical event structures. Such a framework aligns with cognitive psychological theories like event segmentation theory and theory of event coding, positing that human cognition inherently segments continuous experiences into discrete, actionable events. By demonstrating the network's ability to infer these segments, the paper provides a promising pathway towards the realization of artificial systems capable of human-like reasoning and abstraction.

Practical Implications and Future Directions

Practically, \METHOD* can be instrumental in varied fields such as robotics, autonomous vehicle navigation, and adaptive control systems, given its robust mechanism for autonomous context inference and goal-directed adaptability. The adaptability to different operational contexts without prior knowledge or specific design makes it a potential candidate for complex real-world applications where environments or tasks are not fully known a priori.

Future research should aim to explore deeper hierarchies in similar systems, potentially incorporating techniques such as policy gradient methods for event-specific routine optimization. Additionally, the integration of curiosity-driven exploration mechanisms combined with current inference schemes may enhance the system’s ability to explore and learn in unknown environments more effectively.

In conclusion, "Learning, Planning, and Control in a Monolithic Neural Event Inference Architecture" represents a significant step towards autonomous control systems that mimic human cognitive structures. Its ability to infer and adapt to contextual changes in dynamic systems is a testament to the potential of integrating advanced neural architectures with theories of cognitive psychology and active inference principles.