  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.
