Deep reinforcement learning has demonstrated increasing capabilities for
continuous control problems, including agents that can move with skill and
agility through their environment. An open problem in this setting is that of
developing good strategies for integrating or merging policies for multiple
skills, where each individual skill is a specialist in a specific skill and its
associated state distribution. We extend policy distillation methods to the
continuous action setting and leverage this technique to combine expert
policies, as evaluated in the domain of simulated bipedal locomotion across
different classes of terrain. We also introduce an input injection method for
augmenting an existing policy network to exploit new input features. Lastly,
our method uses transfer learning to assist in the efficient acquisition of new
skills. The combination of these methods allows a policy to be incrementally
augmented with new skills. We compare our progressive learning and integration
via distillation (PLAID) method against three alternative baselines.

本文研究在连续控制问题中如何将多个专项技能的策略组合在一起，为了解决这个问题作者扩展了策略蒸馏方法并通过模拟双足步态跨越不同类地形的领域中评估了该技术，同时还引入了一种输入注入方法来利用新的输入特征，最后，作者使用迁移学习来协助高效地获得新的技能。作者将自己的逐步学习方法与三种替代基线进行了比较。