Entity Abstraction in Visual Model-Based Reinforcement Learning (1910.12827v5)

Abstract: This paper tests the hypothesis that modeling a scene in terms of entities and their local interactions, as opposed to modeling the scene globally, provides a significant benefit in generalizing to physical tasks in a combinatorial space the learner has not encountered before. We present object-centric perception, prediction, and planning (OP3), which to the best of our knowledge is the first fully probabilistic entity-centric dynamic latent variable framework for model-based reinforcement learning that acquires entity representations from raw visual observations without supervision and uses them to predict and plan. OP3 enforces entity-abstraction -- symmetric processing of each entity representation with the same locally-scoped function -- which enables it to scale to model different numbers and configurations of objects from those in training. Our approach to solving the key technical challenge of grounding these entity representations to actual objects in the environment is to frame this variable binding problem as an inference problem, and we develop an interactive inference algorithm that uses temporal continuity and interactive feedback to bind information about object properties to the entity variables. On block-stacking tasks, OP3 generalizes to novel block configurations and more objects than observed during training, outperforming an oracle model that assumes access to object supervision and achieving two to three times better accuracy than a state-of-the-art video prediction model that does not exhibit entity abstraction.

• The paper introduces OP3, a novel framework that learns unsupervised entity representations from raw visual inputs to enhance RL generalization.
• It employs a probabilistic inference algorithm to dynamically bind visual objects to latent entity variables, achieving 82% accuracy in novel block-stacking tasks.
• OP3 outperforms baselines by two to three times through uniform entity abstraction, setting new benchmarks for model-based reinforcement learning.

Entity Abstraction in Visual Model-Based Reinforcement Learning

The paper introduces a novel framework called Object-Centric Perception, Prediction, and Planning (OP3), designed for enhancing the generalization capacity of model-based reinforcement learning (RL) systems. The primary hypothesis examined is that modeling environments through entities and their interactions, rather than as a holistic scene, significantly improves generalization to novel tasks that the learning algorithm has not encountered.

Key Contributions and Methodology

OP3 is characterized by its use of an entity-centric approach to model-based RL, distinguishing itself as the first fully probabilistic framework of its kind that learns entity representations from raw visual observations without supervision. The core processes—perception, prediction, and planning—are conditioned on these entity representations, allowing for dynamic involvement of objects observed in visual data. This method further incorporates the concept of entity abstraction, which enforces uniform processing of each entity through identically scoped functions, thus facilitating scalability across varied numbers and configurations of objects.

The implementation of OP3 involves a sophisticated interactive inference algorithm. This algorithm addresses the variable binding problem by inferring which visual objects correspond to latent entity variables, using temporal continuity and feedback from interactions to refine this mapping. A model-based approach, OP3 leverages latent variables as abstracts of entities, grounded through iterative inference methods, enabling OP3 to adapt to new tasks by predicting and planning in the latent space of these entity variables.

Experimental Results

Empirical evaluations demonstrate that OP3 considerably outperforms several baselines, including models that do not use entity abstraction or rely on supervised signals for object segmentation. Notably, the framework attained two to three times better accuracy than state-of-the-art video prediction models when tasked with solving block-stacking problems with novel configurations and numbers of blocks. The experiments reveal OP3's enhanced ability to model diverse environments, including those characterized by dynamic interactions and compositional tasks.

Furthermore, a comparison with models like SAVP and O2P2 underscores the superiority of OP3, achieving an accuracy of 82% without supervision, surpassing the oracle model O2P2, which required access to object segmentations. These quantitative outcomes highlight OP3's potential in deploying reinforcement learning where unseen object configurations are prevalent.

Implications and Future Directions

The theoretical and practical implications of this work suggest that integrating principles of entity abstraction into neural network architectures can significantly potentiate zero-shot learning and transfer learning capabilities these models in complex environments. This approach aligns well with cognitive theories suggesting that human generalization often involves abstract reasoning about discrete objects and their interactions.

Future research directions can explore extending OP3's framework to handle more granular tasks that involve finer object manipulations or more sophisticated interaction dynamics. Bridging these models with attention mechanisms could further enhance interpretability and efficiency, facilitate deployment in real-world robotics scenarios, and improve robustness against occlusion or visual ambiguity.

By advancing the domain of model-based RL with such specialized entity-centric frameworks, OP3 sets a precedent for more sophisticated, adaptable machine learning systems capable of complex, intelligent interactions with their environments.