exploration phase
- North America > United States > California > Los Angeles County > Los Angeles (0.14)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- North America > United States > Michigan (0.04)
MO-DDN: A Coarse-to-Fine Attribute-based Exploration Agent for Multi-object Demand-driven Navigation
The process of satisfying daily demands is a fundamental aspect of humans' daily
Bandit Learning with Positive Externalities
Virag Shah, Jose Blanchet, Ramesh Johari
Neural Information Processing Systems http://nips.cc/
- North America > United States > California > Santa Clara County > Palo Alto (0.04)
- North America > Canada > Quebec > Montreal (0.04)
- Europe > United Kingdom > England > Oxfordshire > Oxford (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- North America > United States > California > Santa Clara County > Palo Alto (0.04)
- North America > Canada (0.04)
- North America > Canada (0.04)
- Europe > Spain (0.04)
- Information Technology > Artificial Intelligence > Machine Learning (0.94)
- Information Technology > Data Science > Data Mining > Big Data (0.68)
- Information Technology > Communications > Networks (0.67)
- Information Technology > Security & Privacy (0.52)
- Leisure & Entertainment > Games (0.45)
- Energy > Energy Storage (0.45)
- Government > Military (0.34)
Linear Stochastic Bandits Under Safety Constraints
Sanae Amani, Mahnoosh Alizadeh, Christos Thrampoulidis
Neural Information Processing Systems http://nips.cc/
- North America > United States > California > Santa Barbara County > Santa Barbara (0.04)
- North America > United States > Florida > Broward County > Fort Lauderdale (0.04)
- North America > Canada (0.04)
- Europe > Spain > Catalonia > Barcelona Province > Barcelona (0.04)
SupplementaryMaterial: RegretinOnlineRecommendationSystems
The probabilities that a user likes each item are unknown.
- Europe > Sweden > Stockholm > Stockholm (0.04)
- Asia > South Korea > Daejeon > Daejeon (0.04)
- North America > United States (0.04)
- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)
OnReward-FreeReinforcementLearningwith LinearFunctionApproximation
During the exploration phase, an agent collects samples without using a pre-specified reward function. After the exploration phase, a reward function is given, and the agent uses samples collected during the exploration phase to computeanear-optimalpolicy.
- North America > United States > Pennsylvania > Allegheny County > Pittsburgh (0.04)
- Europe > United Kingdom (0.04)
- North America > United States > Massachusetts > Middlesex County > Belmont (0.04)
- (2 more...)
OnReward-FreeReinforcementLearningwith LinearFunctionApproximation
During the exploration phase, an agent collects samples without using a pre-specified reward function. After the exploration phase, a reward function is given, and the agent uses samples collected during the exploration phase to computeanear-optimalpolicy.
- North America > United States > Pennsylvania > Allegheny County > Pittsburgh (0.04)
- North America > United States > Massachusetts > Middlesex County > Belmont (0.04)
- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)
- (2 more...)