Learning Modular Robotic Control via Reinforcement Learning using Attention based Global State Prediction

Francis, Brian

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Learning Modular Robotic Control via Reinforcement Learning using Attention based Global State Prediction

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Deep Reinforcement Learning (DRL) has shown remarkable success in the control of single-robot applications. The approach has seen impressive results when applied to multi-robot coordination, but it has some notable shortcomings to overcome. Even though it is becoming increasingly popular for real-world multi-robot autonomy, DRL struggles as the complexity of the control system being developed increases. In systems with a high number of agents and consequently Degrees-of-Freedom (DOF), the training process can be prohibitively time-consuming or fraught with issues that make it difficult to learn optimal behaviors. One of the primary issues that DRL is faced with in multi-robot systems is managing the simultaneous learning process where the inter-agent interactions provide inconsistent information to the model. We investigated Attention-based Global State Prediction (AGSP) which uses information from neighbors to form a belief over the outcome of all the agents in order to overcome this instability in the training process. AGSP is able to predict future states accurately even over a large number of agents using information communicated about the collective actions. We used AGSP in a decentralized modular locomotion task and empirically evaluated the emergent properties. We found that AGSP produces policies that exhibit superior stability and adaptability. This makes AGSP a useful tool for developing safe and consistent controllers with low rates of failure.

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