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AnyMorph: Learning Transferable Polices By Inferring Agent Morphology
Brandon Trabucco · mariano phielipp · Glen Berseth

Tue Jul 19 10:50 AM -- 10:55 AM (PDT) @ Room 309
in Reinforcement Learning: Deep/Batch/Offline »

The prototypical approach to reinforcement learning involves training policies tailored to a particular agent from scratch for every new morphology.Recent work aims to eliminate the re-training of policies by investigating whether a morphology-agnostic policy, trained on a diverse set of agents with similar task objectives, can be transferred to new agents with unseen morphologies without re-training. This is a challenging problem that required previous approaches to use hand-designed descriptions of the new agent's morphology. Instead of hand-designing this description, we propose a data-driven method that learns a representation of morphology directly from the reinforcement learning objective.Ours is the first reinforcement learning algorithm that can train a policy to generalize tonew agent morphologies without requiring a description of the agent's morphology in advance. We evaluate our approach on the standard benchmark for agent-agnostic control, and improve over the current state of the art in zero-shot generalization to new agents. Importantly, our method attains good performance without an explicit description of morphology.

Keywords: Deep Learning · Reinforcement Learning · RL: Deep RL

Author Information

Brandon Trabucco (Carnegie Mellon University)
mariano phielipp (Intel AI Labs)
Glen Berseth (Universite de Montreal/Mila)

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