'Approximate policy iteration' diaporamas de présentation

Reinforcement Learning : Learning Algorithms

1.17k views • 72 slides

Batch RL Via Least Squares Policy Iteration

Batch RL Via Least Squares Policy Iteration. Alan Fern. * Based in part on slides by Ronald Parr. Overview. Motivation LSPI Derivation from LSTD Experimental results. Online versus Batch RL. Online RL: integrates data collection and optimization

589 views • 42 slides

Batch RL Via Least Squares Policy Iteration

Batch RL Via Least Squares Policy Iteration. Alan Fern. * Based in part on slides by Ronald Parr. Overview. Motivation LSPI Derivation from LSTD Experimental results. Online versus Batch RL. Online RL: integrates data collection and optimization

561 views • 42 slides

Cognitive Development in Infancy

843 views • 37 slides

Knowledge Representation Meets Stochastic Planning

Bob Givan Joint work w/ Alan Fern and SungWook Yoon. Knowledge Representation Meets Stochastic Planning. Electrical and Computer Engineering Purdue University. Overview. We present a form of approximate policy iteration specifically designed for large relational MDPs .

355 views • 30 slides

Classifier-Based Approximate Policy Iteration

312 views • 21 slides

Some Final Thoughts

Some Final Thoughts. Abhijit Gosavi. From MDPs to SMDPs. The Semi- MDP is a more general model in which the time for transition is also a random variable. The MDP Bellman equations can be extended to SMDPs to accommodate time. SMDPs (contd.).

202 views • 7 slides