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Compared with traditional model predictive control (MPC) or ZMP methods, reinforcement learning policies require no explicit modeling of dynamic equations and can automatically discover efficient motion patterns through massive trial and error.
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9/7/2026
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UnverifiedSupervised fine-tuning trains models to imitate correct answers labeled by humans, while reinforcement learning fine-tuning allows models to learn optimal strategies through trial and error.70% similarVerifiedReinforcement learning learns optimal policies through continuous interaction between an agent and its environment; its mathematical foundation is the Markov Decision Process (MDP), which consists of a state space, action space, transition function, reward function, and discount factor.69% similarUnverifiedModern VRP solution methods typically combine metaheuristic algorithms (such as genetic algorithms and simulated annealing) with machine learning prediction models to approximate the optimal solution within an acceptable timeframe.69% similarUnverifiedModel-based reinforcement learning improves sample efficiency by learning a dynamics model of the environment for planning, with representative methods including Dreamer and MBPO.68% similar
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