Fully Distributed Multi-Robot Collision Avoidance via Deep Reinforcement Learning for Safe and Efficient Navigation in Complex Scenarios

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In this paper, we present a decentralized sensor-level collision avoidance policy for multi-robot systems, which shows promising results in practical applications.

• In particular, our policy directly maps raw sensor measurements to an agent's steering commands in terms of the movement velocity.
• As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to learn an optimal policy.
• The policy is trained over a large number of robots in rich, complex environments simultaneously using a policy gradient based reinforcement learning algorithm.