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Deep reinforcement learning has demonstrated increasing capabilities forcontinuous control problems, including agents that can move with skill andagility through their environment. An open problem in this setting is that ofdeveloping good strategies for integrating or merging policies for multipleskills, where each individual skill is a specialist in a specific skill and itsassociated state distribution. We extend policy distillation methods to thecontinuous action setting and leverage this technique to combine expertpolicies, as evaluated in the domain of simulated bipedal locomotion acrossdifferent classes of terrain. We also introduce an input injection method foraugmenting an existing policy network to exploit new input features. Lastly,our method uses transfer learning to assist in the efficient acquisition of newskills. The combination of these methods allows a policy to be incrementallyaugmented with new skills. We compare our progressive learning and integrationvia distillation (PLAID) method against three alternative baselines.

Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control