Proximal Policy Optimization and its Dynamic Version for Sequence Generation
Table 1: Results of different algorithms on counting task.
['Algorithms MLE', 'Algorithms MLE', 'precision 87.01']
, we compares the precision and learning curves of different algorithms on the counting task. We find out that the training progress of PPO is slower than PPO-dynamic, which validates that dynamic constraints can facilitate the training.