parameter-noise

OpenAI presents parameter noise, a technique that adds adaptive noise to neural network policy parameters rather than action spaces, enabling agents to learn tasks significantly faster than traditional action noise approaches. The method achieves 2x faster learning on HalfCheetah and represents a middle ground between evolution strategies and deep RL approaches like TRPO and DDPG.