# Decaying Clipping Range in Proximal Policy Optimization

arXiv: https://arxiv.org/abs/2102.10456 · HTML: https://mlsanity.com/paper/2102.10456/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 20 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Benchmarking Deep Reinforcement Learning for Continuous Control](https://mlsanity.com/paper/1604.06778/) (2016)
- [Sample Efficient Actor-Critic with Experience Replay](https://mlsanity.com/paper/1611.01224/) (2016)
- [Learning Continuous Control Policies by Stochastic Value Gradients](https://mlsanity.com/paper/1510.09142/) (2015)
- [Trust Region-Guided Proximal Policy Optimization](https://mlsanity.com/paper/1901.10314/) (2019)
- [An Adaptive Clipping Approach for Proximal Policy Optimization](https://mlsanity.com/paper/1804.06461/) (2018)

## Built on by (most cited first, 6 of 6)

- [Discovering Temporally-Aware Reinforcement Learning Algorithms](https://mlsanity.com/paper/2402.05828/) (2024)
- [Proximal Policy Optimization with Adaptive Threshold for Symmetric Relative Density Ratio](https://mlsanity.com/paper/2203.09809/) (2022)
- [ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning](https://mlsanity.com/paper/2409.18827/) (2024)
- [BandPO: Bridging Trust Regions and Ratio Clipping via Probability-Aware Bounds for LLM Reinforcement Learning](https://mlsanity.com/paper/2603.04918/) (2026)
- [Beyond Fixed Morphologies: Learning Graph Policies with Trust Region Compensation in Variable Action Spaces](https://mlsanity.com/paper/2508.14102/) (2025)
- [From Kinematics to Dynamics: Learning to Refine Hybrid Plans for Physically Feasible Execution](https://mlsanity.com/paper/2604.12474/) (2026)

## Abstract (arXiv)

Proximal Policy Optimization (PPO) is among the most widely used algorithms in reinforcement learning, which achieves state-of-the-art performance in many challenging problems. The keys to its success are the reliable policy updates through the clipping mechanism and the multiple epochs of minibatch updates. The aim of this research is to give new simple but effective alternatives to the former. For this, we propose linearly and exponentially decaying clipping range approaches throughout the training. With these, we would like to provide higher exploration at the beginning and stronger restrictions at the end of the learning phase. We investigate their performance in several classical control and locomotive robotic environments. During the analysis, we found that they influence the achieved rewards and are effective alternatives to the constant clipping method in many reinforcement learning tasks.