Better exploration with parameter noise

Researchers found that adding noise to policy parameters, rather than actions, can drive better exploration in reinforcement learning.

In HalfCheetah, parameter-space noise led to much faster learning and about double the performance after 20 episodes versus action-noise training.

The method worked without an extra policy head, making it simpler and cheaper to train than standard exploration approaches.