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)
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 (7)
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%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%