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In continuous control, the goal of reinforcement learning is to learn a policy function π(a|s) that maps observations to actions, and to iteratively improve it by maximizing the cumulative discounted return.
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10/30/2026
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UnverifiedPolicy gradient methods directly parameterize the policy π(a|s) and maximize expected return through gradient ascent, naturally supporting continuous action spaces and stochastic policies, with representative algorithms including REINFORCE and PPO.77% 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.72% similarUnverifiedPolicy iteration and value function estimation are two fundamental solver frameworks in reinforcement learning.70% similarVerifiedThe essence of reinforcement learning is to have the model continuously optimize to maximize a certain reward signal.70% similarUnverifiedΨ-0.6 acquires through reinforcement learning a scheduling policy that adaptively selects among different representations, and the model learns during training to judge which reasoning path suits the current task.70% similar
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