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Hamiltonian Primal-Dual Policy Reinforcement Learning for Derivative-Free Model-Based Control
Finite-horizon optimal control becomes difficult when a dynamical model can be simulated reliably, but its derivatives are unavailable, discontinuous, or unreliable. Hamiltonian Primal-Dual Policy Optimization (HPDPO) is introduced as a derivative-free, model-based reinforcement learning framework f
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