Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality
This addresses the need for diverse policies in RL for applications like exploration and robustness, representing an incremental improvement over existing multi-objective methods.
The paper tackles the problem of finding diverse yet near-optimal policies in reinforcement learning, proposing DOMiNO, which discovers policies with different state occupancies while maintaining high reward performance, as demonstrated in domains like the DeepMind Control Suite.
Finding different solutions to the same problem is a key aspect of intelligence associated with creativity and adaptation to novel situations. In reinforcement learning, a set of diverse policies can be useful for exploration, transfer, hierarchy, and robustness. We propose DOMiNO, a method for Diversity Optimization Maintaining Near Optimality. We formalize the problem as a Constrained Markov Decision Process where the objective is to find diverse policies, measured by the distance between the state occupancies of the policies in the set, while remaining near-optimal with respect to the extrinsic reward. We demonstrate that the method can discover diverse and meaningful behaviors in various domains, such as different locomotion patterns in the DeepMind Control Suite. We perform extensive analysis of our approach, compare it with other multi-objective baselines, demonstrate that we can control both the quality and the diversity of the set via interpretable hyperparameters, and show that the discovered set is robust to perturbations.