  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.
