Description

1 Introduction
The goal of this assignment is to do experiments with Monte-Carlo(MC) Learning and Temporal-Difference(TD) Learning. MC and TD methods learn directly from episodes of experience without knowledge of MDP model. TD method can learn after every step, while MC method requires a full episode to update value evaluation. Your goal is to implement MC and TD methods and test them in the small gridworld.
2 Small Gridworld

Figure 1: Gridworld
As shown in Fig.1, each grid in the gridwold represents a certain state. Let st denotes the state at grid t. Hence the state space can be denoted as S = {st|t ∈ 0,..,35}. S1 and S35 are terminal states, where the others are nonterminal states and can move one grid to north, east, south and west. Hence the action space is A = {n,e,s,w}. Note that actions leading out of the grid leave state unchanged. Each movement get a reward of -1 until the terminal state is reached.
3 Experiment Requirments
• Programming language: python3
• You should implement both first-visit and every-visit MC method and TD(0) to evaluate an uniform random policy π(n|·) = π(e|·) = π(s|·) = π(w|·) = 0.25.
4 Report and Submission
• Your reports and source files (.py) should be compressed and named after “studentID+name”.