'Model free' presentation slideshows

CS 188: Artificial Intelligence Spring 2007

486 views • 36 slides

CS 188: Artificial Intelligence Spring 2007

438 views • 36 slides

CS 188: Artificial Intelligence Spring 2007

366 views • 36 slides

CS 5368: Artificial Intelligence Fall 2010

425 views • 35 slides

Reinforcement Learning

Reinforcement Learning. Problem: Find values for fixed policy ? (policy evaluation) Model-based learning : Learn the model, solve for values Model-free learning : Solve for values directly (by sampling). The Story So Far: MDPs and RL. Techniques:. Things we know how to do:.

652 views • 27 slides

Fitted/ batch /model-based RL: A (sketchy, biased) overview(?)

330 views • 26 slides

Reinforcement Learning

Presented by: Kyle Feuz. Reinforcement Learning. Outline. Motivation MDPs RL Model-Based Model-Free Q-Learning SARSA Challenges. Examples. Pac-Man Spider. MDPs. 4-tuple (State, Actions, Transitions, Rewards). Important Terms. Policy Reward Function Value Function Model.

269 views • 25 slides

World models and basis functions

World models and basis functions. Nisheeth 25 th January 2019. Levels of analysis. RL in the brain. What is the problem? Reinforcement ? learning preferences for actions that lead to desirable outcomes How is it solved?

229 views • 22 slides

To Model or not To Model; that is the question.

328 views • 20 slides

RL in the brain

RL in the brain. Nisheeth 24 th January 2019. Temporal difference learning. Consider the Q-learning update Or the SARSA update A generic temporal difference principle can be discerned for behavioral reinforcement. The TD learning principle.

162 views • 16 slides

Cpt_S 580-03 Intelligent Agents

Cpt_S 580-03 Intelligent Agents. (Reinforcement Learning). Actions: Wave, Stand, Clap Observations: Colors, Rewards Goal: find an optimal policy. What is your policy?. What does the world look like?. Formalize the Problem. Knowns Unknowns. (UP) Wave (RIGHT) Stand (DOWN) Clap.

201 views • 14 slides

Introduction to Reinforcement Learning

Introduction to Reinforcement Learning. Freek Stulp. Overview. General principles of RL Markov Decision Process as model Values of states: V(s) Values of state-actions: Q(a,s) Exploration vs. Exploitation Issues in RL Conclusion.

367 views • 12 slides

Detecting Conflict-Related Changes in the ACC

Z = 4.0. Z = 4.0. Red. Trial 1 - Congruent. Z = 1.6. Z = 1.6. Fixation. Blue. Trial 2 - Congruent. Fixation. Yellow. Trial 3 - Incongruent. Time. Fixation. Green. Trial 4 - Incongruent. ITI=0.5 - 7.0 s. Incongruent Trials. Congruent Trials. Frequency. RT. #682.16 RR89.

166 views • 1 slides