Approximating Gradients for Differentiable Quality Diversity in Reinforcement Learning

We propose two variants of the current state-of-the-art DQD algorithm that compute gradients via approximation methods common in reinforcement learning (RL). Evaluation on simulated locomotion tasks indicates our method to achieve comparable performance compared to state-of-the-art.