Local online training on resource-constrained platforms requires low computational and training-state overhead.
We study the numerical stability and computational efficiency of half-precision proximal policy optimization (PPO) on commodity GPUs.
Directly converting PPO training to FP16 is unreliable because numerical errors feed back through policy updates and subsequent data collection, while PPO repeatedly compares a current policy with a fixed behavior policy.
We propose PPO-FP16 with three structure-aware modules.
Global scalar protection stabilizes statistics shared across a minibatch or parameter update.
Policy-change residual protection extracts and preserves small differences between the current and behavior policies.
Efficient persistent-update protection stabilizes optimizer state and parameter writeback while reducing state storage.
These changes preserve the PPO objective, hyperparameters, and training budget.
Across six continuous-control tasks and five seeds, PPO-FP16 achieves the lowest mean return-AUC rank (1.90) among the four methods and ranks first in 17 of 30 environment-seed comparisons.
Eight of nine performance configurations yield PPO update speedups ranging from 1.16 to 2.03 times.
Relative to Full FP32 Adam, optimizer-state memory is reduced by 71.7 to 74.9 percent, and peak allocated GPU memory is reduced by up to 44.5 percent.
The results show that protecting a small set of structurally important quantities can deliver stable, efficient FP16 PPO without retuning.