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The research object is a finite Markov decision process (MDP) whose trajectories are generated by a fixed behavior policy, with the state-action chain being irreducible and allowing for periodicity.
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10/12/2026
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Related Claims
UnverifiedThis is a theoretically oriented study whose conclusions rest on assumptions such as finite MDPs and fixed behavior policies, and it remains distant from the function approximation setting of deep reinforcement learning.78% similarVerifiedThe Markov Decision Process (MDP) provides a complete mathematical framework for reinforcement learning, with its Markov property holding that future states depend only on the current state and action.74% 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.73% similarVerifiedA Markov Decision Process (MDP) consists of five core elements: state space, action space, state transition probability, reward function, and discount factor.72% similarUnverifiedThe key assumption of a Markov Decision Process (MDP) is the memoryless property, meaning the next state depends only on the current state and action, independent of the historical path72% similar
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