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Constrained Markov decision processes (CMDPS) are extensions to Markov decision process (MDPs). There are three fundamental differences between MDPs and CMDPs. [15] There are multiple costs incurred after applying an action instead of one. CMDPs are solved with linear programs only, and dynamic programming does not work.
A partially observable Markov decision process (POMDP) is a generalization of a Markov decision process (MDP). A POMDP models an agent decision process in which it is assumed that the system dynamics are determined by an MDP, but the agent cannot directly observe the underlying state.
The decentralized partially observable Markov decision process (Dec-POMDP) [1] [2] is a model for coordination and decision-making among multiple agents. It is a probabilistic model that can consider uncertainty in outcomes, sensors and communication (i.e., costly, delayed, noisy or nonexistent communication).
Value function approximation is a critical component to solving Markov decision processes (MDPs) defined over a continuous state space. A good function approximator allows a reinforcement learning (RL) agent to accurately represent the value of any state it has experienced, without explicitly storing its value.
The theory of Markov decision processes states that if is an optimal policy, we act optimally (take the optimal action) by choosing the action from (,) with the highest action-value at each state, . The action-value function of such an optimal policy ( Q π ∗ {\displaystyle Q^{\pi ^{*}}} ) is called the optimal action-value function and is ...
Discrete-time Markov decision processes (MDP) are planning problems with: durationless actions, nondeterministic actions with probabilities, full observability, maximization of a reward function, and a single agent. When full observability is replaced by partial observability, planning corresponds to a partially observable Markov decision ...
This process is used for modeling and regulation of dynamic systems, especially under uncertainty, and is commonly addressed using methods like Markov decision processes (MDPs) and dynamic programming.
Dean played a leading role in the adoption of the framework of Markov decision processes (MDPs) as a foundational tool in artificial intelligence. In particular, he pioneered the use of AI representations and algorithms for || factoring || complex models and problems into weakly-interacting subparts to improve computational efficiency.