A distributed zeroth-order policy gradient algorithm allows networked agents to collaboratively optimize policies using only local human preference feedback on H-horizon trajectory pairs from kappa-hop neighborhoods, with proven convergence to an epsilon-stationary point.
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Distributed Zeroth-Order Policy Gradient for Networked Multi-agent Reinforcement Learning from Human Feedback
A distributed zeroth-order policy gradient algorithm allows networked agents to collaboratively optimize policies using only local human preference feedback on H-horizon trajectory pairs from kappa-hop neighborhoods, with proven convergence to an epsilon-stationary point.