'State action pair' diaporamas de présentation

Reinforcement Learning: A Tutorial

Reinforcement Learning: A Tutorial. Rich Sutton AT&T Labs -- Research www.research.att.com/~sutton. Outline. Core RL Theory Value functions Markov decision processes Temporal-Difference Learning Policy Iteration The Space of RL Methods Function Approximation Traces Kinds of Backups

594 views • 54 slides

CS 224S LING 281 Speech Recognition and Synthesis

CS 224S LING 281 Speech Recognition and Synthesis. Lecture 15: Dialogue and Conversational Agents (III) Dan Jurafsky. New Idea: Modeling a dialogue system as a probabilistic agent. A conversational agent can be characterized by: The current knowledge of the system

562 views • 42 slides

Eligibility Traces (ETs) Week #7

Eligibility Traces (ETs) Week #7. Introduction. A basic mechanism of RL. ? in TD( ? ) refers to an eligibility trace. TD methods such as Sarsa and Q-learning may be combined with ETs to obtain more efficient learning methods. Two different ways to view ETs: Forward view Backward view.

576 views • 38 slides

Reinforcement Learning

443 views • 37 slides

Artificial Intelligence Chapter 21. The Situation Calculus

Artificial Intelligence Chapter 21. The Situation Calculus. Outline. Reasoning about States and Actions Some Difficulties Generating Plans Additional Readings and Discussion. 21.1 Reasoning about States and Actions.

308 views • 21 slides

Purposive Behavior Acquisition for a real robot by vision based Reinforcement Learning

Purposive Behavior Acquisition for a real robot by vision based Reinforcement Learning. Minuru Asada,Shoichi Noda, Sukoya Tawarasudia, Koh Hosoda Presented by: Subarna Sadhukhan. Reinforced learning.

233 views • 14 slides