Statements (49)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:academic
|
| gptkbp:appliesTo |
gptkb:reinforcement_learning
multi-agent systems |
| gptkbp:concerns |
repeated games
stochastic games convergence to equilibrium evolutionary game dynamics exploration vs exploitation opponent modeling |
| gptkbp:focusesOn |
adaptation in strategic environments
|
| gptkbp:hasApplication |
gptkb:evolutionary_computation
behavioral economics cybersecurity network security robotics resource allocation supply chain management market design social network analysis algorithmic trading multi-agent reinforcement learning distributed control auction design energy grid management traffic routing adversarial AI automated negotiation competitive AI cooperative AI evolutionary biology modeling resource management in wireless networks |
| https://www.w3.org/2000/01/rdf-schema#label |
Learning in Games
|
| gptkbp:includes |
gptkb:fictitious_play
learning algorithms Nash equilibrium learning Q-learning in games regret minimization |
| gptkbp:relatedTo |
gptkb:machine_learning
game theory |
| gptkbp:studiedBy |
gptkb:economist
gptkb:mathematician biologists computer scientists |
| gptkbp:studies |
how agents learn strategies in games
|
| gptkbp:usedIn |
gptkb:artificial_intelligence
economics evolutionary biology |
| gptkbp:bfsParent |
gptkb:David_K._Levine
|
| gptkbp:bfsLayer |
7
|