MPC is used to optimize control inputs by approximating a reference trajectory using dt, N and T variables in a finite(2-3 seconds) time-horizon. This reference trajectory should encompass very small values of dt which will be multiplied by N (should be a number that when multiplied by dt is 2-3Continue Reading
Learns a policy which tells an agent what action to take under what circumstances. Q-learning learns a policy that is optimal in the sense that it maximizes the expected value of the total reward over any and all successive steps, starting from the current state.Continue Reading
* Is a discrete time stochastic control process for decision making in situations where outcomes are partly random and partly under the control of a decision maker. * At each discrete time step, the process is in some state s, and the decision maker may choose any action a thatContinue Reading