Constrained Variational Policy Optimization for Safe Reinforcement Learning (2201.11927v3)

Abstract: Safe reinforcement learning (RL) aims to learn policies that satisfy certain constraints before deploying them to safety-critical applications. Previous primal-dual style approaches suffer from instability issues and lack optimality guarantees. This paper overcomes the issues from the perspective of probabilistic inference. We introduce a novel Expectation-Maximization approach to naturally incorporate constraints during the policy learning: 1) a provable optimal non-parametric variational distribution could be computed in closed form after a convex optimization (E-step); 2) the policy parameter is improved within the trust region based on the optimal variational distribution (M-step). The proposed algorithm decomposes the safe RL problem into a convex optimization phase and a supervised learning phase, which yields a more stable training performance. A wide range of experiments on continuous robotic tasks shows that the proposed method achieves significantly better constraint satisfaction performance and better sample efficiency than baselines. The code is available at https://github.com/liuzuxin/cvpo-safe-rl.

• The paper introduces CVPO, an EM-based algorithm that reformulates safe RL as a probabilistic inference problem to integrate safety constraints effectively.
• It demonstrates significant improvements in training stability and sample efficiency, achieving up to 1000 times greater efficiency than on-policy baselines.
• The method secures robust constraint adherence and optimality guarantees, outperforming prior approaches like SAC-Lag and TRPO-Lag in diverse robotic control tasks.

Constrained Variational Policy Optimization for Safe Reinforcement Learning

The paper "Constrained Variational Policy Optimization for Safe Reinforcement Learning" explores improving policy learning in reinforcement learning (RL) under safety constraints. The primary goal of safe RL is to deploy policies that maximize the task reward while ensuring constraint violations do not exceed a pre-defined threshold. Traditional approaches have utilized the primal-dual framework, which involves transforming constrained optimization problems into unconstrained variants, but these methods often struggle with numerical instability and lack robust optimality guarantees. This research proposes an innovative solution to these challenges by reframing the safe RL problem as a probabilistic inference task.

Methodology and Theoretical Contributions

The authors introduce the Constrained Variational Policy Optimization (CVPO) algorithm, structured around an Expectation-Maximization (EM) approach which seamlessly integrates safety constraints into policy optimization. The process is composed of two key phases:

1. E-step (Expectation step): A non-parametric variational distribution is optimized with respect to expected rewards, ensuring it adheres to safety constraints and KL-divergence trust regions. The dual formulation is shown to be convex, granting strong duality and optimality guarantees, a characteristic often absent in prior primal-dual methods.
2. M-step (Maximization step): This phase involves improving the policy by fitting it to the variational distribution obtained in the E-step, employing a supervised learning approach with KL regularization. By conducting policy updates in this fashion, the authors ensure robustness and mitigate overfitting risks.

Empirical Results

Performance evaluations conducted on various robotic control tasks highlighted CVPO's strengths. The approach resulted in significantly more stable training processes and enriched sample efficiency compared to baseline approaches. Noteworthily, it demonstrated superior constraint satisfaction with fewer violations, up to 1000 times more sample-efficient than on-policy baselines. This comparison involved established methods such as SAC-Lag, TRPO-Lag, and CPO, affirming the efficacy of CVPO in both on-policy and off-policy settings. The experimental data underscores CVPO's ability to achieve high task rewards while maintaining strict adherence to safety constraints.

Implications and Future Directions

The paper's contributions extend the understanding of reinforcement learning as a probabilistic inference problem, introducing novel methodologies that enhance policy optimization stability and efficiency in safe RL contexts. The robustness guarantees and scalability to off-policy scenarios pave the way for practical applications in real-world environments, especially where safety is paramount.

Future avenues of research may explore scalable computational strategies tailored to further improve the algorithm's efficiency given its computational intensity. Additionally, enhancing critic networks to predict constraint violation costs more accurately could yield further performance benefits. In theoretical domains, extending the insights gained to other forms of RL as inference problems, could present new perspectives in managing complex dynamic systems.

In summary, CVPO represents a significant advance in safe reinforcement learning, yielding robust, optimal, and sample-efficient policies capable of being employed in diverse, safety-critical applications. It not only enhances the stability of learning but also offers a promising direction for achieving reliable RL deployments in real-world settings.