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OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments (2306.08649v2)

Published 14 Jun 2023 in cs.LG, cs.AI, and cs.CV

Abstract: Cognitive science and psychology suggest that object-centric representations of complex scenes are a promising step towards enabling efficient abstract reasoning from low-level perceptual features. Yet, most deep reinforcement learning approaches only rely on pixel-based representations that do not capture the compositional properties of natural scenes. For this, we need environments and datasets that allow us to work and evaluate object-centric approaches. In our work, we extend the Atari Learning Environments, the most-used evaluation framework for deep RL approaches, by introducing OCAtari, that performs resource-efficient extractions of the object-centric states for these games. Our framework allows for object discovery, object representation learning, as well as object-centric RL. We evaluate OCAtari's detection capabilities and resource efficiency. Our source code is available at github.com/k4ntz/OC_Atari.

Citations (13)

Summary

  • The paper introduces a novel framework that applies object-centric state representations to Atari 2600 games, enhancing RL training efficiency.
  • It leverages Atari's RAM for real-time object tracking, achieving faster processing and high detection fidelity compared to pixel-based methods.
  • The modular design and object-centric dataset (ODA) facilitate dynamic scenario manipulation and transparent, robust agent learning in varied environments.

An Overview of OCAtari: Object-Centric Atari Reinforcement Learning Environments

The paper discusses OCAtari, a novel framework providing object-centric state representations for the Atari 2600 games environment, traditionally used as a benchmark for evaluating deep reinforcement learning (RL) algorithms. This framework addresses a notable gap in the current landscape where object-centric approaches and representations, emphasized by cognitive science and psychology for their abstraction capabilities, have not been sufficiently integrated or evaluated in reinforcement learning paradigms focused on Atari games.

Core Contributions

  1. Framework Introduction: OCAtari builds upon the Arcade Learning Environment (ALE) to track and provide real-time, object-centric insights into Atari games. This approach contrasts existing methods that rely heavily on high-dimensional pixel inputs with minimal abstraction.
  2. RAM-Based Object Tracking: The framework leverages the Atari games' RAM to extract and maintain a list of objects interacting within the game environment. This RAM-centric approach enables faster state representation processing, notably more efficient than pixel-based methods, promising significant computational savings without substantially sacrificing the detection accuracy.
  3. Object-Centric Dataset (ODA): The authors have introduced ODA to benchmark object discovery and learning methods within OCAtari, providing a standardized dataset that features comprehensive object tracking from the RAM and vision modes across various games.
  4. Modularity and Flexibility: OCAtari allows manipulation of RAM states to dynamically alter game scenarios or create new challenges. This capability can be pivotal for developing RL agents with robust adaptive and generalization capabilities in varied environments.

Experimental Evaluation

OCAtari's performance was validated through rigorous experimental metrics, including detection precision, recall, and F1-scores across multiple Atari games, showcasing high detection fidelity and operational efficiency. The framework’s proficiency in seamlessly transitioning between object-centric and traditional pixel approaches validates its flexibility for diverse RL scenarios.

Practical Implications and Theoretical Insights

OCAtari’s design facilitates empirical studies on how object-centric views can impact RL policies' efficiency, interpretability, and generalization. By decoupling object identification from policy development, researchers could derive more transparent agent architectures, aligning computational exploration with human-like reasoning strategies. An immediate benefit from OCAtari's RAM-based extraction is the acceleration of object-centric agent training, reducing resource consumption significantly.

Future Directions

Looking forward, integrating OCAtari with model-based RL architectures may yield greater insights into the role of object-centric state abstraction in model-driven decision processes. Additionally, exploring how RAM-based object representations can be harnessed to tackle issues like sparse rewards and exploration-exploitation balance could present valuable advancements in agent robustness.

In summary, OCAtari introduces an essential tool for researchers aiming to venture into object-centric RL in a well-established testbed, the Atari 2600, bridging perceptual gaps between agent processing and human-like scene understanding. This work sets the stage for future exploration into deeper, more interpretable, and efficient object-centric methodologies for reinforcement learning.

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