Enterprise AI Analysis

Introducing STORI: A Benchmark for Uncertainty

To bridge the gap between idealized benchmarks and real-world challenges, this research introduces STORI (STOchastic-ataRI), a novel benchmark designed to systematically incorporate diverse stochastic effects into classic Atari environments. STORI provides fine-grained control over various sources of uncertainty, enabling a more rigorous and comprehensive evaluation of RL algorithms.

By extending the Arcade Learning Environment (ALE) with controllable stochasticity, STORI allows researchers to probe agent robustness and adaptability under varied forms of uncertainty, from action-dependent noise to complex concept drifts and partial observations. This controlled introduction of real-world complexities is crucial for developing AI agents that can reliably operate in dynamic enterprise settings.

The benchmark's design facilitates: 1) Identification of specific failure modes for different RL approaches under uncertainty, 2) Development of new algorithms robust to specific stochastic challenges, and 3) Comparison of methods using a consistent, yet challenging, evaluation framework.

Unified Taxonomy of Stochasticity in RL Environments

Alongside the STORI benchmark, this work proposes an updated and expanded taxonomy of stochasticity in RL environments. This framework provides a unified language for classifying and understanding the different ways uncertainty can manifest, which is essential for systematic research and development.

The taxonomy categorizes stochasticity into several types:

• Deterministic (Type 0): Predictable outcomes based on state and action.
• Intrinsic Stochasticity (Types 1-3): Randomness inherent in the environment dynamics, either action-dependent (e.g., sticky actions), action-independent (e.g., random environmental events), or due to concept drift (e.g., changing rules over time).
• Partially Observed Environments (Types 4-5): Uncertainty arising from incomplete information, either requiring representation learning from partial observations or dealing with specific missing state variables (e.g., hidden game elements).

This detailed classification allows for a more precise analysis of how different RL algorithms cope with specific forms of uncertainty, guiding the design of more robust and specialized AI solutions for enterprise applications.

Key Insights from Experimental Evaluation

Experiments using STORI compared DreamerV3 and STORM, two state-of-the-art model-based RL algorithms. The results consistently showed a marked decline in performance for both agents when stochasticity was introduced, compared to deterministic baselines. This highlights the significant challenge uncertainty poses to even advanced RL systems.

Key observations include:

• Sensitivity to Action Space: Environments with small, sensitive action spaces (like Breakout) experienced a greater performance drop under uncertainty, as incorrect actions have immediate, severe consequences. In contrast, environments with redundant actions or recovery opportunities (like Boxing) mitigated performance loss.
• Algorithm Robustness: While DreamerV3 often outperformed STORM in default deterministic settings, STORM frequently exhibited slightly stronger performance across various stochasticity types, suggesting a greater inherent robustness to unpredictable environmental dynamics.
• Adaptation in Partial Observability: In certain partially observed scenarios (e.g., hidden screen portions in Boxing), agents were able to adapt by leveraging spatial constraints, effectively turning limited visibility into a manageable challenge rather than a prohibitive handicap. Interestingly, in some cases, the absence of non-essential state variables (e.g., hidden score) even improved performance, simplifying representation learning.

These findings underscore the complex interplay between environment characteristics, stochasticity types, and algorithm design in achieving robust AI performance.

Enterprise Implications & Future Research Directions

The STORI benchmark and its taxonomy provide invaluable tools for advancing enterprise AI. By enabling systematic evaluation under diverse uncertainties, businesses can develop and deploy RL solutions with greater confidence in their real-world performance. The insights gained are critical for applications in autonomous systems, complex decision-making, and resource optimization, where operational robustness is paramount.

For example, in manufacturing, an agent controlling a robotic arm must contend with sensor noise (Type 2 stochasticity) and potential equipment wear (Type 3 concept drift). In financial trading, market conditions are inherently stochastic and partially observable. STORI allows for the development and testing of agents specifically designed for these complex, high-stakes environments.

Future work will involve expanding the STORI benchmark to include more games and stochasticity modes, further exploring the interplay between different uncertainty types, and evaluating a wider range of model-free and model-based algorithms. This will further refine our understanding of algorithm robustness and guide the development of truly resilient AI systems capable of thriving in unpredictable enterprise landscapes.