  A key aspect of intelligence is the ability to demonstrate a broad spectrum
of behaviors for adapting to unexpected situations. Over the past decade,
advancements in deep reinforcement learning have led to groundbreaking
achievements to solve complex continuous control tasks. However, most
approaches return only one solution specialized for a specific problem. We
introduce Quality-Diversity Actor-Critic (QDAC), an off-policy actor-critic
deep reinforcement learning algorithm that leverages a value function critic
and a successor features critic to learn high-performing and diverse behaviors.
In this framework, the actor optimizes an objective that seamlessly unifies
both critics using constrained optimization to (1) maximize return, while (2)
executing diverse skills. Compared with other Quality-Diversity methods, QDAC
achieves significantly higher performance and more diverse behaviors on six
challenging continuous control locomotion tasks. We also demonstrate that we
can harness the learned skills to adapt better than other baselines to five
perturbed environments. Finally, qualitative analyses showcase a range of
remarkable behaviors: adaptive-intelligent-robotics.github.io/QDAC.
