MC control, Sarsa, Q-learning
Our goal in this paper is to adaptively choose the learning rate for TD learning with linear function approximation by observing the evolution of the function ...
Deep Reinforcement LearningWe see a) TD(0) only updated the last state, b) TD(?) updated the trajectory in this episode, and c) ET(?) additionally updated trajectories ... off-policy deep RLIn this work, the C35 steel was pack-borided in the temperature range of 800?. 1000°C for a time duration ranging from 0.5 to 8 h. Lecture 8: Integrating Learning and Planning - David SilverWe demonstrate in a variety of policy evaluation tasks that this simple adaptive algorithm performs competitively with the best approach in hindsight,.
Autres Cours:
