Deep Reinforcement Learning

We 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 RL
In 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 Silver
We demonstrate in a variety of policy evaluation tasks that this simple adaptive algorithm performs competitively with the best approach in hindsight,.
Artificial Neural Networks: RL2 - EPFL
On-Policy TD Control: Sarsa. ?? learn q? and improve ? while following ?. Updates: Q(St,At) ? Q(St,At) + ?[Rt+1 + ?Q(St+1,At+1) ? Q(St,At)].

Deep Reinforcement Learning

Autres Cours:

MC control, Sarsa, Q-learning