Challenges of Real-World Reinforcement Learning (1904.12901v1)

Abstract: Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is beginning to show some successes in real-world scenarios. However, much of the research advances in RL are often hard to leverage in real-world systems due to a series of assumptions that are rarely satisfied in practice. We present a set of nine unique challenges that must be addressed to productionize RL to real world problems. For each of these challenges, we specify the exact meaning of the challenge, present some approaches from the literature, and specify some metrics for evaluating that challenge. An approach that addresses all nine challenges would be applicable to a large number of real world problems. We also present an example domain that has been modified to present these challenges as a testbed for practical RL research.

• The paper identifies nine distinct challenges that hinder the transition from simulated environments to practical reinforcement learning deployments.
• It introduces a batch RL framework with off-policy evaluation to enhance sample efficiency and handle complex, high-dimensional state-action spaces.
• The study emphasizes safety, explainability, and real-time inference, offering actionable metrics and methodologies to guide future RL research.

Challenges of Real-World Reinforcement Learning

The paper "Challenges of Real-World Reinforcement Learning" by Gabriel Dulac-Arnold, Daniel Mankowitz, and Todd Hester presents a discourse on the practical difficulties involved in deploying RL systems in real-world applications, extending beyond the theoretical constructs typically addressed in controlled experimental settings. The authors delineate nine specific challenges that impede the transition from research to production, providing a comprehensive analysis and suggesting evaluation metrics to guide further investigations.

Identified Challenges

1. Off-line and Off-Policy Training: Typically, RL algorithms rely on simulated environments for expansive data collection, but real-world systems often necessitate learning from fixed logs generated by existing control systems. The paper describes a batch RL framework to adapt to this constraint and highlights off-policy evaluation methods, like importance sampling, to ensure policy improvements without real-world execution.
2. Sample Efficiency: Real-world systems impose limitations owing to the cost of interactions and limited availability of identical environments for distributed learning. The authors suggest metrics such as data required to achieve specified performance thresholds, indicating sample efficiency.
3. High-Dimensional State and Action Spaces: Many real-world systems, such as industrial control systems and robotics, involve vast state and action spaces. Approaches like action elimination and policy distillation into actionable formats are explored to manage this complexity effectively.
4. Safety Constraints: Real-world implementations must adhere to safety constraints to avoid system failures and human risk. The paper advocates for frameworks like Constrained Markov Decision Processes (CMDPs) and highlights the criticality of quantifying safety through metrics such as constraint violations.
5. Partial Observability and Non-Stationarity: Real systems frequently present partial observability and non-stationary dynamics. Approaches utilizing recurrent networks and domain randomization are reviewed to address these challenges, ensuring robustness across varying conditions.
6. Unspecified and Multi-Objective Reward Functions: In practice, systems often require balancing multiple objectives, which necessitates integrating multi-objective evaluations and understanding policy trade-offs, handled through CVaR objectives and learning from sub-goal distributions.
7. Explainability: For RL to gain trust from operators, policies must be interpretable. Techniques to distill policies into human-readable forms are scrutinized, and the significance of qualitative explainability assessments is underscored.
8. Real-Time Inference: Systems require real-time decision-making, posing constraints on computational resources for policy execution, an aspect particularly noted in fields like robotics and immediate recommender systems.
9. System Delays: Delays in control feedback loops present challenges for both actuation and reward collection. Approaches to manage delayed outcomes and retrospective reward assignment are examined to ensure effective policy optimization.

Implications and Future Directions

The outlined challenges emphasize the divergence between theoretical RL development and practical deployment. A significant implication of this work is the call-to-action for RL research to transcend laboratory environments, considering real-world complexities such as safety, robustness, and computational constraints.

The paper discusses an experimental setup derived from the DeepMind Control Suite to emulate these challenges, serving both as a benchmark for algorithmic evaluations and a stimulus for innovative solutions that address these complexities holistically.

Future RL advancements must integrate model-based and ensemble methods for improved sample efficiency and robustness. Furthermore, human-in-the-loop systems must be refined for reward function formulation and policy explainability, ensuring stakeholder alignment with RL system actions.

In conclusion, while the individual challenges presented have been explored in isolation, the complete set forms a formidable frontier for RL research with real-world aspirations. Practitioners and theorists alike should leverage these insights to propel RL toward becoming a cornerstone technology in diverse application domains.