Reinforcement Learning
"Reinforcement learning is learning what to do – how to map situations to actions – so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most forms of machine learning, but instead must discover which actions yield the most reward by trying them."
– Sutton, Richard S. and Andrew G. Barto. Reinforcement Learning: An Introduction. (1.1). MIT Press, Cambridge, MA, 1998.
DeepInverseQ-learningwithConstraints
Neural Information Processing Systems
Acompact summary comparing thedifferent properties of the various approaches mentioned above can be found in Table 1.
- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.14)
- Europe > Germany > Baden-Württemberg > Freiburg (0.05)
- North America > United States > California > Santa Clara County > Stanford (0.04)
- (2 more...)
- North America > Puerto Rico (0.04)
- North America > Canada > Quebec > Montreal (0.04)
- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (1.00)
- Europe > Germany > Hesse > Darmstadt Region > Darmstadt (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- Europe > United Kingdom > England > Greater London > London (0.04)
- Asia > Japan > Honshū > Chūbu > Toyama Prefecture > Toyama (0.04)
- Information Technology > Artificial Intelligence > Robots (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (1.00)
- (2 more...)
- North America > United States > California (0.04)
- North America > United States > Pennsylvania > Allegheny County > Pittsburgh (0.04)
- North America > United States > New Jersey (0.04)
- (3 more...)
- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Control Systems (0.68)
- Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (0.46)
- North America > United States > Massachusetts > Middlesex County > Cambridge (0.04)
- North America > United States > Massachusetts > Hampshire County > Amherst (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- Health & Medicine > Therapeutic Area > Neurology (0.46)
- Leisure & Entertainment > Games (0.46)
- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (1.00)
Whento Askfor Help: Proactive Interventionsin Autonomous Reinforcement Learning
Neural Information Processing Systems
Wheneverthe 6 Figure 4:Asubsetof Irreversible (left) andoffofthemiddle). Asafe estimates 7 ourevaluationtasks: Tabletop Manipulation, Peg Insertion, and Half-Cheetah Velocity.
- North America > United States > California (0.04)
- North America > Canada > Quebec > Montreal (0.04)
- Europe > United Kingdom > England > Greater London > London (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- Information Technology > Artificial Intelligence > Robots (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (1.00)
- (2 more...)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- Asia > Middle East > Jordan (0.04)
Nearly Horizon-Free Offline Reinforcement Learning
Neural Information Processing Systems
A (potentially is =( 1, 2, H), where h : S ! ItholdsVh(s)depends ˆP(s0|s, a), ho S factor.