Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning (2409.12045v2)

Abstract: Safety is one of the key issues preventing the deployment of reinforcement learning techniques in real-world robots. While most approaches in the Safe Reinforcement Learning area do not require prior knowledge of constraints and robot kinematics and rely solely on data, it is often difficult to deploy them in complex real-world settings. Instead, model-based approaches that incorporate prior knowledge of the constraints and dynamics into the learning framework have proven capable of deploying the learning algorithm directly on the real robot. Unfortunately, while an approximated model of the robot dynamics is often available, the safety constraints are task-specific and hard to obtain: they may be too complicated to encode analytically, too expensive to compute, or it may be difficult to envision a priori the long-term safety requirements. In this paper, we bridge this gap by extending the safe exploration method, ATACOM, with learnable constraints, with a particular focus on ensuring long-term safety and handling of uncertainty. Our approach is competitive or superior to state-of-the-art methods in final performance while maintaining safer behavior during training.

• The paper presents an innovative extension of ATACOM by learning long-term, task-specific constraints directly from data.
• It introduces a Feasibility Value Function using a distributional RL perspective to estimate cumulative constraint violations over an infinite horizon.
• The method incorporates a Conditional Value-at-Risk approach and adaptive thresholding, outperforming other safe RL methods in various challenging settings.

Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning

Overview

The paper "Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning" addresses significant challenges in the practical deployment of Reinforcement Learning (RL) techniques for real-world robotic applications. Traditional RL approaches often struggle with safety constraints and uncertainty, particularly in environments where constraints are complex, task-specific, or unknown a priori. This paper proposes an innovative method by enhancing the safe exploration method, ATACOM, with learnable constraints that ensure long-term safety while managing uncertainty.

Key Contributions

• Learnable Constraints for Long-Term Safety: The primary contribution of this work is the extension of the ATACOM method to learn task-specific, long-term constraints directly from data. This allows the method to adapt to complex and dynamic environments where predefined constraints are impractical or unknown.
• Distributional RL Perspective: The framework integrates a distributional RL perspective to model constraints' uncertainty, thereby providing an estimate of the total uncertainty in the learned policy. This is realized through the introduction of the Feasibility Value Function (FVF), which accounts for expected cumulative constraint violations over an infinite horizon.
• Risk-Aware Policy: The incorporation of Conditional Value-at-Risk (CVaR) allows for the derivation of a risk-aware policy. This aspect is crucial for ensuring that the RL agent not only considers the mean performance but also the variability and potential risk in extreme scenarios.

Detailed Methodology

Feasibility Value Function and Long-Term Safety

The Feasibility Value Function (FVF) is a novel construct that estimates the expected discounted cumulative constraint violation. This contrasts with traditional approaches that typically focus on immediate or short-term safety. The FVF is defined using the BeLLMan operator in a manner similar to value functions in standard RL, but it specifically addresses safety by focusing on constraint violations.

Distributional Value Iteration

To handle the uncertainty in the learned constraints, the paper employs a distributional approach to the FVF. This involves approximating the distribution of future costs, represented as a Gaussian in this paper. The mean and variance of the FVF are updated using a 2-Wasserstein distance, providing a comprehensive estimate of the safety constraint distribution. This distributional perspective is crucial for formulating and enforcing risk-sensitive safety constraints.

Adaptive Constraint Threshold

The dynamic adjustment of the constraint threshold (δ) is another critical innovation. This adaptive mechanism tunes the threshold based on cumulative episodic cost, thus maintaining a balance between exploration and safety. This automatic tuning addresses one of the key challenges in Safe RL—setting appropriate constraint thresholds manually, which can be task-specific and difficult to determine.

Experimental Evaluation

The proposed method is evaluated on three distinct environments:

1. Cartpole: Demonstrates the method's ability to handle simple yet dynamically complex environments. It outperforms state-of-the-art Safe RL methods in terms of learning speed while maintaining fewer constraint violations compared to most baselines.
2. Navigation: Highlights the method's efficacy in a scenario with moving obstacles and partial observability. The approach effectively balances performance and safety, outperforming other methods by achieving superior task performance with minimal constraint violations.
3. 3-DoF Robot Air Hockey: This environment tests the method in a complex control task where constraints are both kinematic and dynamic. Here, the method significantly reduces violations while achieving competitive task performance.

Implications and Future Directions

The method presented in this paper bridges a crucial gap between model-free and model-based Safe RL approaches. By incorporating learnable constraints and handling uncertainty from a distributional perspective, the paper lays a foundation for more robust and adaptable RL algorithms suitable for complex real-world applications. Potential future directions include:

• Integration with Known Constraints: Exploring how to effectively merge learned constraints with any available predefined constraints.
• Real-World Deployment: Enhancing the method for direct deployment in real robot scenarios, possibly through techniques like domain randomization and sim-to-real transfer.
• Scalability to Higher Dimensions: Investigating the scalability of the approach to high-dimensional robotic tasks, such as those involving multiple degrees of freedom or intricate human-robot interactions.

Conclusion

This paper makes a significant advancement in Safe Reinforcement Learning by proposing a novel method that learns long-term safety constraints and manages uncertainty through a distributional RL approach. The proposed framework shows promise in ensuring safer exploration and robust policy performance in complex environments, positioning it as a substantial contribution to the field of safe and reliable RL applications.

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