MDP
A mathematical framework for modeling sequential decision-making, providing the formal structure for the perception–decision–action loop of intelligent agents. It is defined by four components: state space, action space, transition probabilities, and reward function. MDP serves as the theoretical foundation for AI systems such as AlphaGo and autonomous driving.
Core Facts
Timeline (last 90 days)
The sequential auction framework models multi-step tasks as an MDP, where each state represents a partially completed task, agents bid for the right to take the next action, and after the auction clears the winner executes and updates the state, looping until the task is complete.
A Semi-Markov Decision Process (Semi-MDP) is an extension of the standard MDP, distinguished by the fact that action execution time can be variable rather than a fixed single-step timestamp.
The paper assumes access to a generative model of the MDP, meaning the algorithm can query samples for any state-action pair.
Partially observable environments correspond to Partially Observable Markov Decision Processes (POMDPs), which are computationally far more difficult than fully observable Markov Decision Processes (MDPs).
策略梯度定理表明期望回报对策略参数的梯度可表示为 ∇J(θ) = E[∇log π_θ(a|s) · A(s,a)]，其中A是优势函数
MDP假设当前状态包含做出最优决策所需的全部信息（马尔可夫性质），而POMDP中智能体无法直接观测完整的环境状态
The theoretical foundations of the Agent Loop can be traced back to the BDI (Belief-Desire-Intention) architecture of the 1980s and the agent-environment interaction loop of the Markov Decision Process (MDP) in reinforcement learning.
All Facts (8)
Reinforcement 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.
80%UnverifiedThe theoretical foundations of the Agent Loop can be traced back to the BDI (Belief-Desire-Intention) architecture of the 1980s and the agent-environment interaction loop of the Markov Decision Process (MDP) in reinforcement learning.
60%UnverifiedThe sequential auction framework models multi-step tasks as an MDP, where each state represents a partially completed task, agents bid for the right to take the next action, and after the auction clears the winner executes and updates the state, looping until the task is complete.
50%UnverifiedA Semi-Markov Decision Process (Semi-MDP) is an extension of the standard MDP, distinguished by the fact that action execution time can be variable rather than a fixed single-step timestamp.
50%UnverifiedThe paper assumes access to a generative model of the MDP, meaning the algorithm can query samples for any state-action pair.
50%UnverifiedPartially observable environments correspond to Partially Observable Markov Decision Processes (POMDPs), which are computationally far more difficult than fully observable Markov Decision Processes (MDPs).
50%Unverified策略梯度定理表明期望回报对策略参数的梯度可表示为 ∇J(θ) = E[∇log π_θ(a|s) · A(s,a)]，其中A是优势函数
50%UnverifiedMDP假设当前状态包含做出最优决策所需的全部信息（马尔可夫性质），而POMDP中智能体无法直接观测完整的环境状态
50%