Statements (52)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:mathematical_concept
game theory concept |
| gptkbp:alsoKnownAs |
stochastic games
|
| gptkbp:generalizes |
repeated games
Markov decision processes |
| gptkbp:hasApplication |
gptkb:machine_learning
control theory economics network security operations research robotics multi-agent systems resource allocation |
| gptkbp:hasComponent |
states
actions rewards transition probabilities players policies |
| gptkbp:hasProperty |
can be discrete or continuous
state transitions depend on current state and actions of all players can be finite or infinite horizon can be zero-sum or general-sum can have perfect or imperfect information outcome is stochastic players choose actions simultaneously rewards depend on state and actions solution concepts include Nash equilibrium solution concepts include correlated equilibrium solution concepts include Markov perfect equilibrium |
| https://www.w3.org/2000/01/rdf-schema#label |
Markov games
|
| gptkbp:introduced |
gptkb:Lloyd_Shapley
|
| gptkbp:introducedIn |
1953
|
| gptkbp:relatedTo |
gptkb:Nash_equilibrium
Markov chain dynamic programming repeated games non-cooperative games cooperative games zero-sum games |
| gptkbp:studiedBy |
gptkb:economist
computer scientists game theorists |
| gptkbp:studiedIn |
multi-agent reinforcement learning
|
| gptkbp:type |
gptkb:stochastic_process
dynamic game |
| gptkbp:usedIn |
gptkb:artificial_intelligence
economics operations research |
| gptkbp:bfsParent |
gptkb:Finite_stochastic_games
gptkb:Zero-sum_stochastic_games |
| gptkbp:bfsLayer |
7
|