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FlowDPG: Deterministic Policy Gradient on Flow Matching Policies for Real-World Manipulation
Real-world reinforcement learning for robotic manipulation remains challenging, and this difficulty is amplified for flow matching policies: policy gradients must be backpropagated through time (BPTT) along the multi-step ODE that maps noise to actions, which is computationally prohibitive and numer
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