Keywords: reinforcement learning, exploration, parameter noise
TL;DR: Parameter space noise allows reinforcement learning algorithms to explore by perturbing parameters instead of actions, often leading to significantly improved exploration performance.
Abstract: Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent's parameters, which can lead to more consistent exploration and a richer set of behaviors. Methods such as evolutionary strategies use parameter perturbations, but discard all temporal structure in the process and require significantly more samples. Combining parameter noise with traditional RL methods allows to combine the best of both worlds. We demonstrate that both off- and on-policy methods benefit from this approach through experimental comparison of DQN, DDPG, and TRPO on high-dimensional discrete action environments as well as continuous control tasks.
Code: [![Papers with Code](/images/pwc_icon.svg) 10 community implementations](https://paperswithcode.com/paper/?openreview=ByBAl2eAZ)
Data: [OpenAI Gym](https://paperswithcode.com/dataset/openai-gym)
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 14 code implementations](https://www.catalyzex.com/paper/arxiv:1706.01905/code)