mlsanityData to 14 Oct 2026

Incentivizing Safer Actions in Policy Optimization for Constrained Reinforcement Learning

arXiv 2509.09208 ↗First posted 11 Sep 2025cs.LG cs.AIcited by 0

On the map: Deep reinforcement learning methods → Safe and constrained RL

Abstract (arXiv)

Constrained Reinforcement Learning (RL) aims to maximize the return while adhering to predefined constraint limits, which represent domain-specific safety requirements. In continuous control settings, where learning agents govern system actions, balancing the trade-off between reward maximization and constraint satisfaction remains a significant challenge. Policy optimization methods often exhibit instability near constraint boundaries, resulting in suboptimal training performance. To address this issue, we introduce a novel approach that integrates an adaptive incentive mechanism in addition to the reward structure to stay within the constraint bound before approaching the constraint boundary. Building on this insight, we propose Incrementally Penalized Proximal Policy Optimization (IP3O), a practical algorithm that enforces a progressively increasing penalty to stabilize training dynamics. Through empirical evaluation on benchmark environments, we demonstrate the efficacy of IP3O compared to the performance of state-of-the-art Safe RL algorithms. Furthermore, we provide theoretical guarantees by deriving a bound on the worst-case error of the optimality achieved by our algorithm.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods. Builds on 25 of its 43 references that are in the map. First posted on arXiv 11 Sep 2025.

Builds on

25 of 43 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Exterior Penalty Policy Optimization with Penalty Metric Network under Constraints22 Jul

Other references · 24

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015Trust Region Policy Optimizationcited by 2,580
  3. 2015Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)cited by 1,070
  4. 2017Constrained Policy Optimizationcited by 983
  5. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  6. 2015End-to-End Training of Deep Visuomotor Policiescited by 875
  7. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 721
  8. 2017Value-Decomposition Networks For Cooperative Multi-Agent Learningcited by 503
  9. 2018Reward Constrained Policy Optimizationcited by 417
  10. 2015Doubly Robust Off-policy Value Evaluation for Reinforcement Learningcited by 339
  11. 2015Risk-Constrained Reinforcement Learning with Percentile Risk Criteriacited by 337
  12. 2020Responsive Safety in Reinforcement Learning by PID Lagrangian Methodscited by 316
12 more
  1. 2019Reinforcement Learning in Healthcare: A Surveycited by 310
  2. 2021MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learningcited by 290
  3. 2020Projection-Based Constrained Policy Optimizationcited by 203
  4. 2017A General Safety Framework for Learning-Based Control in Uncertain Robotic Systemscited by 163
  5. 2023Safety-Gymnasium: A Unified Safe Reinforcement Learning Benchmarkcited by 143
  6. 2019IPO: Interior-point Policy Optimization under Constraintscited by 132
  7. 2023OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Researchcited by 80
  8. 2017Continuously Differentiable Exponential Linear Unitscited by 73
  9. 2022Penalized Proximal Policy Optimization for Safe Reinforcement Learningcited by 68
  10. 2022Constrained Update Projection Approach to Safe Policy Optimizationcited by 58
  11. 2019Convergent Policy Optimization for Safe Reinforcement Learningcited by 57
  12. 2022Evaluating Model-free Reinforcement Learning toward Safety-critical Taskscited by 22

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  4. 2024Safe and Balanced: A Framework for Constrained Multi-Objective Reinforcement Learning
  5. 2025Viability of Future Actions: Robust Safety in Reinforcement Learning via Entropy Regularization
  6. 2026Boundary-Seeking Policy Gradient for Safe Reinforcement Learning
  7. 2026BCPPO: Bachelier-Inspired Constrained Proximal Policy Optimization for Tail-Risk-Aware Safe Reinforcement Learning
  8. 2025Constraint-Aware Reinforcement Learning via Adaptive Action Scaling