One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control

Jul 12, 2020

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Reinforcement learning is typically concerned with learning control policies tailored to a particular agent. We investigate whether there exists a single policy that generalizes to controlling a wide variety of agent morphologies - ones in which even dimensionality of state and action spaces changes. Such a policy would distill general and modular sensorimotor patterns that can be applied to control arbitrary agents. We propose a policy expressed as a collection of identical modular neural network components for each of the agent’s actuators. Every module is only responsible for controlling its own actuator and receives information from its local sensors. In addition, messages are passed between modules, propagating information between distant modules. A single modular policy can successfully generate locomotion behaviors for over 20 planar morphologies such as monopod hoppers, quadrupeds, bipeds and generalize to variants not seen during training - a process that would normally require training and manual hyper-parameter tuning for each morphology. We observe a wide variety of drastically diverse locomotion styles across morphologies as well as centralized coordination emerging via message passing between decentralized modules purely from the reinforcement learning objective.

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The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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