Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning
This work addresses a bottleneck in QD-RL research by enabling broader application of RL advancements, though it is incremental in extending prior hybrid methods.
The paper tackles the limited integration of deep reinforcement learning (RL) algorithms in hybrid quality-diversity (QD) and RL methods by proposing a unified modular framework, Generalized Actor-Critic QD-RL, which enables new algorithms that solve the humanoid environment previously unsolvable by existing QD-RL approaches.
The synergies between Quality-Diversity (QD) and Deep Reinforcement Learning (RL) have led to powerful hybrid QD-RL algorithms that have shown tremendous potential, and brings the best of both fields. However, only a single deep RL algorithm (TD3) has been used in prior hybrid methods despite notable progress made by other RL algorithms. Additionally, there are fundamental differences in the optimization procedures between QD and RL which would benefit from a more principled approach. We propose Generalized Actor-Critic QD-RL, a unified modular framework for actor-critic deep RL methods in the QD-RL setting. This framework provides a path to study insights from Deep RL in the QD-RL setting, which is an important and efficient way to make progress in QD-RL. We introduce two new algorithms, PGA-ME (SAC) and PGA-ME (DroQ) which apply recent advancements in Deep RL to the QD-RL setting, and solves the humanoid environment which was not possible using existing QD-RL algorithms. However, we also find that not all insights from Deep RL can be effectively translated to QD-RL. Critically, this work also demonstrates that the actor-critic models in QD-RL are generally insufficiently trained and performance gains can be achieved without any additional environment evaluations.