Consider the Markov Decision Process below. Actions have non-deterministic effects, i.e., taking an action in a state returns different states with some probabilities. There are two actions out of...

algo

Extracted text: Consider the Markov Decision Process below. Actions have non-deterministic effects, i.e., taking an action in a state returns different states with some probabilities. There are two actions out of each state: D for development and R for research. Consider the following deterministic ultimately-care-only-about-money reward for any transition resulting at state: State S1 S2 S3 S4 Reward 100 25 50 Assume you start with state S1 and perform the following actions: • Action: R; New State: S3 • Action: D; New State: S2 • Action: R; New State: S1 • Action: R; New State: S3 • Action: R; New State: S4 • Action: D; New State: S2 a) Assume V(S) for all S = S1, S2, S3 and S4 is initialized to 0. Update V(S) for each of the states using the Temporal Difference Algorithm.