[Paper Review] Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning

Yihang Yao, Zuxin Liu+5 more|arXiv (Cornell University)|Oct 5, 2023

Adversarial Robustness in Machine LearningComputer Science3 citations

TL;DR

This paper introduces Conditioned Constrained Policy Optimization (CCPO), a novel reinforcement learning framework that enables zero-shot adaptation to unseen safety constraint thresholds through Versatile Value Estimation (VVE) and Conditioned Variational Inference (CVI). CCPO achieves superior safety and task performance with high data efficiency, outperforming baselines in high-dimensional control tasks without retraining.

ABSTRACT

Safe reinforcement learning (RL) focuses on training reward-maximizing agents subject to pre-defined safety constraints. Yet, learning versatile safe policies that can adapt to varying safety constraint requirements during deployment without retraining remains a largely unexplored and challenging area. In this work, we formulate the versatile safe RL problem and consider two primary requirements: training efficiency and zero-shot adaptation capability. To address them, we introduce the Conditioned Constrained Policy Optimization (CCPO) framework, consisting of two key modules: (1) Versatile Value Estimation (VVE) for approximating value functions under unseen threshold conditions, and (2) Conditioned Variational Inference (CVI) for encoding arbitrary constraint thresholds during policy optimization. Our extensive experiments demonstrate that CCPO outperforms the baselines in terms of safety and task performance while preserving zero-shot adaptation capabilities to different constraint thresholds data-efficiently. This makes our approach suitable for real-world dynamic applications.

Motivation & Objective

• To address the challenge of training versatile safe reinforcement learning policies that adapt to varying safety constraint thresholds without retraining.
• To improve training efficiency by avoiding the need to pre-train on all possible constraint thresholds.
• To enable zero-shot generalization to unseen cost thresholds while maintaining strong safety guarantees and task performance.

Proposed method

• Introduces Versatile Value Estimation (VVE), a value function representation learning method that generalizes Q-values to unseen constraint thresholds using transfer learning principles.
• Employs Conditioned Variational Inference (CVI) to condition the policy on arbitrary constraint thresholds during training, enabling end-to-end differentiable policy optimization.
• Uses a conditional variational inference framework to model the policy as a function of the constraint threshold, allowing the agent to infer behavior policies for new thresholds at inference time.
• Combines VVE and CVI in a unified online training paradigm that jointly optimizes the policy and value functions under conditional constraints.
• Applies convex optimization in the E-step of the inference process to handle constraint conditions efficiently, ensuring safety and stability.
• Employs a behavior policy conditioning mechanism that enables data-efficient generalization to new thresholds without fine-tuning.

Experimental results

Research questions

• RQ1 Can a single safe RL policy be trained to generalize to unseen safety constraint thresholds without retraining or fine-tuning?
• RQ2 How can value functions be estimated for previously unseen constraint thresholds during deployment?
• RQ3 What is the impact of conditioning the policy on constraint thresholds during training on zero-shot adaptation performance?
• RQ4 How does the proposed method compare to baseline constrained RL approaches in terms of safety and data efficiency?
• RQ5 Can the framework maintain high task performance while ensuring low constraint violations across diverse threshold conditions?

Key findings

• CCPO achieves significantly lower cost violations (e.g., 0.59 ± 0.31 on Ball-Circle) compared to ablated versions and baselines, demonstrating strong safety generalization.
• On the Ant-Run task, CCPO maintains high reward (660.88 ± 4.82) with low cost violation (3.13 ± 1.67), while the ablated CCPO w/o VVE drops to 428.59 ± 88.39 reward and 10.66 ± 11.81 cost violation.
• The CCPO w/o VVE variant shows a 25% drop in average reward and over 6x increase in cost violation on Drone-Run, highlighting VVE’s critical role in value generalization.
• The CCPO w/o CVI variant exhibits higher cost violations (e.g., 1.44 ± 0.72 on Ball-Circle) and lower rewards (641.33 ± 40.14), proving CVI’s importance in threshold-conditioned policy learning.
• CCPO generalizes smoothly across threshold conditions, as shown by the consistent performance curve in Figure 2, indicating effective encoding of constraint conditions.
• In high-dimensional state and action spaces (e.g., Ant-Run), all baseline methods fail to achieve safe adaptation, while CCPO maintains robust performance and safety.