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Motivated by humans' ability to adapt skills in the learning of new ones,this paper presents AdaptNet, an approach for modifying the latent space ofexisting policies to allow new behaviors to be quickly learned from like tasksin comparison to learning from scratch. Building on top of a givenreinforcement learning controller, AdaptNet uses a two-tier hierarchy thataugments the original state embedding to support modest changes in a behaviorand further modifies the policy network layers to make more substantivechanges. The technique is shown to be effective for adapting existingphysics-based controllers to a wide range of new styles for locomotion, newtask targets, changes in character morphology and extensive changes inenvironment. Furthermore, it exhibits significant increase in learningefficiency, as indicated by greatly reduced training times when compared totraining from scratch or using other approaches that modify existing policies.Code is available at https://motion-lab.github.io/AdaptNet.

AdaptNet: Policy Adaptation for Physics-Based Character Control