README.md

Machine, Date & Learning

Linear-Programming

Demonstration of linear-programming for solving MDPs.

Team 25 | Tribrid

• Nitin Chandak (2019101024)
• Ayush Sharma (2019101004)

Making A matrix

• We created all possible state in the form of 5-tuple i.e. (pos,mat,arrow,state,health) and store in list as all_state. In total 600 states. Their domains are as follows:

    POS = [ "W","N","E","S","C" ]    # position of IJ
    MAT = [ 0,1,2 ]    # material with IJ
    ARROW = [ 0,1,2,3 ]    # number of arrows
    STATE = [ "D","R" ]    # ready and dormant state of MM
    HEALTH = [ 0,25,50,75,100 ]    # MM’s health

• Then we calculated possbile actions of all states and store in dict as state_actions.

• Created get_pfsb() function that will take (state, action) as parameters & return possible final state list along with it's probability distribution i.e. list of 6-tuple (pos,mat,arrow,state,health, prob).

• Generated map as r_state_map to allocate unique ID to every state.

• Next, we made a 2-D matrix (named it A, it is not our final required A matrix) with dimension len(all_state)*len(all_state), where each cell have data as a list with entry 0's of length len(state_actions[all_state[r]]), where r is a row variable ranging from (0 to len(all_state)).

• Say, if an action(action index = ai) can take you from state A(rownum = j) to B(rownum = i) with probability P. Then we will do the follwing:

    A[j][j][ai] += P
    B[i][j][ai] -= P

• Basically, add the probability to the value at index of state A and subtract from the value at index of state B in the same column of action(action index = ai).

• Next, to create final A matrix (we named it as LP_A in our code), by opening the list at each cell of our 2-D A matrix.

Finding Policy & Result Analysis

• Before, finding the policy, we obtained utility of State-Action pair i.e values X. ValuesX is basically the expected number of time an action can be opted in a given state.

• Values X has been calculated via linear-programming with a linear objective and inequality constraints. LPP for obtaining X is shown:

    maximize RX | with constraint AX  = alpha, X >= 0

• R in above LPP is array of rewards for all actions valid in each and evry state.

• A is matrix described as in above section (matrix which we named LP_A). It represents flow of probability of valid actions for each and every state.

• alpha holds initial probability of states. It is list of zeros with value 1 at index corressponding to state ('C',2,3,'R',100).

• To determine policy for each state, we pick the action with highest corressponding value in X by iterating to all valid X values for each and every state. Following is the code for the same:

    optimal_policy = {}
    cum_i = 0
    for i in range(len(all_state)):
        s = all_state[i]
        max_reward = -1000000
        for j in range(len(state_actions[s])):
            a = state_actions[s][j]
            if max_reward <= R[0][cum_i+j]:
                max_reward = R[0][cum_i+j]
                optimal_policy[s] = a
        cum_i += len(state_actions[s])      

    policy = []
    for s in optimal_policy:
        (pos,mat,arrow,state,health) = s
        sublist = []
        state = [pos[0],mat,arrow,state,health]
        action = optimal_policy[s]
        sublist.append(state)
        sublist.append(action)
        policy.append(sublist)

Multiple Policies

Can there be multiple policies? Why?

Yes, there can be multiple policies. Since, the (state, actions) with the same corressponding value in X are interchangeable. Hence, there can be multiple policies.

What changes can you make in your code to generate another policy?

We can have following two changes that can generate another policy:-

1. In our code we have modification in the condition for finding action having max value of 'X'.The former finds the last highest value of x in case many x's have the same largest value. The second one finds the first x with the highest value. Code for both are as follows:-

        if max_reward <= R[0][cum_i+j]:
            max_reward = R[0][cum_i+j]
            optimal_policy[s] = a

        if max_reward < R[0][cum_i+j]:
            max_reward = R[0][cum_i+j]
            optimal_policy[s] = a

• This change won't be affecting A matrix (which we name LP_A in our code), as it has been formed before performing LP.
• Also, alpha and R vector will remain unaffected for the same reason.

2. Another way to change the generated policy is by changing the order of actions stored in state_actions for each state. For example changing [ 'UP','LEFT','DOWN','RIGHT','STAY' ] to [ 'UP','LEFT','STAY','RIGHT','DOWN' ] will change the last possible highest reward giving action. Say if in current setup all actions have same reward for a state then policy will STAY in first case, but after changing the order the policy will be DOWN.

• It will surely affect the A matrix (which we name LP_A in our code), as it has been formed before performing LP, but depends on the order the action has been stored for each states.
• Same is the case for R vector as it is with A matrix (which we name LP_A in our code) for the same reason.
• As, alpha vector holds the initial status i.e. probability of the states, which won't get updated during algorithm. Hence, no affect on alpha vector.

3. Other way to chane the policy is by changing starting variables like changing the start from ('C',2,3,'R',100) to ('N',0,3,'R',75) or any other state.

• It will only affect the alpha vector as initial probability distribution gets modified. A & R will remain unaffected.

4. We can change the STEPCOST value.

• Only R will get affected as it represents the expected reward for each action valid for each and every state, which will get changed due to change in STEPCOST. alpha & A will remain unaffected.

NOTE: Running command python3 part_3.py will generate an output file in outputs/part_3_output.json. It will contain data of A matrix, R matrix, Alpha matrix, X values,Policy and Objective in following format:

    {
        "a": [
            [
                1.0, 
                0.0, 
                0.0, 
                0.0
            ],
            [
                0.0, 
                1.0, 
                0.0, 
                0.0
            ],
            [
                0.0, 
                0.0, 
                1.0, 
                0.0
            ]
        ],
        "r": [
            1.0, 
            1.0, 
            1.0, 
            1.0
        ],
        "alpha": [
            0, 
            0, 
            0, 
            1
        ],
        "x": [
            1.724, 
            1.784, 
            0.645, 
            1.854
        ],
        "policy": [
            [
                [W,0,0,D,0], "RIGHT"
            ],
            [
                [W,0,0,D,25], "RIGHT"
            ],
                ...
            [
                [C,2,3,R,75], "HIT"
            ],
            [
                [C,2,3,R,100], "SHOOT"
            ]
        ],
        "objective": -11.54321
    }