!pip install aicrowd-cli > /dev/null

import os
import matplotlib.pyplot as plt 
import seaborn as sns

AICROWD_DATASET_PATH = os.getenv("DATASET_PATH", os.getcwd()+"/a5562c7d-55f0-4d06-841c-110655bb04ec_a2_gridworld_inputs.zip")
AICROWD_RESULTS_DIR = os.getenv("OUTPUTS_DIR", "results")
API_KEY = "" # Get your key from https://www.aicrowd.com/participants/me

!unzip -q $AICROWD_DATASET_PATH

DATASET_DIR = 'inputs/'

import numpy as np

class GridEnv_HW2:
    def __init__(self, 
                 goal_location, 
                 action_stochasticity,
                 non_terminal_reward,
                 terminal_reward,
                 grey_in,
                 brown_in,
                 grey_out,
                 brown_out
                ):

        # Do not edit this section 
        self.action_stochasticity = action_stochasticity
        self.non_terminal_reward = non_terminal_reward
        self.terminal_reward = terminal_reward
        self.grid_size = [10, 10]

        # Index of the actions 
        self.actions = {'N': (1, 0), 
                        'E': (0,1),
                        'S': (-1,0), 
                        'W': (0,-1)}
        
        self.perpendicular_order = ['N', 'E', 'S', 'W']
        
        l = ['normal' for _ in range(self.grid_size[0]) ]
        self.grid = np.array([l for _ in range(self.grid_size[1]) ], dtype=object)

        self.grid[goal_location[0], goal_location[1]] = 'goal'
        self.goal_location = goal_location

        for gi in grey_in:
            self.grid[gi[0],gi[1]] = 'grey_in'
        for bi in brown_in:    
            self.grid[bi[0], bi[1]] = 'brown_in'

        for go in grey_out:    
            self.grid[go[0], go[1]] = 'grey_out'
        for bo in brown_out:    
            self.grid[bo[0], bo[1]] = 'brown_out'

        self.grey_outs = grey_out
        self.brown_outs = brown_out

    def _out_of_grid(self, state):
        if state[0] < 0 or state[1] < 0:
            return True
        elif state[0] > self.grid_size[0] - 1:
            return True
        elif state[1] > self.grid_size[1] - 1:
            return True
        else:
            return False

    def _grid_state(self, state):
        return self.grid[state[0], state[1]]        
        
    def get_transition_probabilites_and_reward(self, state, action):
        """ 
        Returns the probabiltity of all possible transitions for the given action in the form:
        A list of tuples of (next_state, probability, reward)
        Note that based on number of state and action there can be many different next states
        Unless the state is All the probabilities of next states should add up to 1
        """

        grid_state = self._grid_state(state)
        
        if grid_state == 'goal':
            return [(self.goal_location, 1.0, 0.0)]
        elif grid_state == 'grey_in':
            npr = []
            for go in self.grey_outs:
                npr.append((go, 1/len(self.grey_outs), 
                            self.non_terminal_reward))
            return npr
        elif grid_state == 'brown_in':
            npr = []
            for bo in self.brown_outs:
                npr.append((bo, 1/len(self.brown_outs), 
                            self.non_terminal_reward))
            return npr
        
        direction = self.actions.get(action, None)
        if direction is None:
            raise ValueError("Invalid action %s , please select among" % action, list(self.actions.keys()))

        dir_index = self.perpendicular_order.index(action)
        wrap_acts = self.perpendicular_order[dir_index:] + self.perpendicular_order[:dir_index]
        next_state_probs = {}
        for prob, a in zip(self.action_stochasticity, wrap_acts):
            d = self.actions[a]
            next_state = (state[0] + d[0]), (state[1] + d[1])
            if self._out_of_grid(next_state):
                next_state = state
            next_state_probs.setdefault(next_state, 0.0)
            next_state_probs[next_state] += prob

        npr = []
        for ns, prob in next_state_probs.items():
            next_grid_state = self._grid_state(ns)
            reward = self.terminal_reward if next_grid_state == 'goal' else self.non_terminal_reward
            npr.append((ns, prob, reward))

        return npr

    def step(self, state, action):
        npr = self.get_transition_probabilites_and_reward(state, action)
        probs = [t[1] for t in npr]
        sampled_idx = np.random.choice(range(len(npr)), p=probs)
        sampled_npr = npr[sampled_idx]
        next_state = sampled_npr[0]
        reward = sampled_npr[2]
        is_terminal = next_state == tuple(self.goal_location)
        return next_state, reward, is_terminal

def get_base_kwargs():
    goal_location = (9,9)
    action_stochasticity = [0.8, 0.2/3, 0.2/3, 0.2/3]
    grey_out = [(3,2), (4,2), (5,2), (6,2)]
    brown_in = [(9,7)]
    grey_in = [(0,0)]
    brown_out = [(1,7)]
    non_terminal_reward = 0
    terminal_reward = 10

    base_kwargs =  {"goal_location": goal_location, 
            "action_stochasticity": action_stochasticity,
            "brown_in": brown_in, 
            "grey_in": grey_in, 
            "brown_out": brown_out,
            "non_terminal_reward": non_terminal_reward,
            "terminal_reward": terminal_reward,
            "grey_out": grey_out,}
    
    return base_kwargs

base_kwargs = get_base_kwargs()

def value_iteration(env, gamma):
    # Initial Values
    values = np.zeros((10, 10))

    # Initial policy
    policy = np.empty((10, 10), object)
    policy[:] = 'N' # Make all the policy values as 'N'

    # Begin code here
    extra_info = {'delta':[]}  #Put your extra information needed for plots etc in this dictionary
    delta=1e-8
    brown_in_loc = np.where(env.grid == 'brown_in')
    brown_out_loc = np.where(env.grid == 'brown_out')
    grey_in_loc = np.where(env.grid == 'grey_in')
    state_brown_in = brown_in_loc[0][0], brown_in_loc[1][0]   
    state_brown_out = brown_out_loc[0][0], brown_out_loc[1][0]   
    state_grey_in = grey_in_loc[0][0], grey_in_loc[1][0]   
    extra_info['brown_in']=[]
    extra_info['brown_out']=[]
    extra_info['grey_in']=[]
    possible_actions=list(env.actions.keys())
    while (delta>=1e-8):  #repeat until delta reduces less than 1e-8 
        delta=0.0
        vg,pl = np.zeros((10, 10)),np.empty((10, 10), object)
        for x in range(env.grid_size[0]):  #go over all x-coordinates
            for y in range(env.grid_size[1]):  #go over all y-coordinates
                temp_J=[]
                for a in possible_actions:  #go over all possible actions
                    nextstates_probs_rews=env.get_transition_probabilites_and_reward((x,y),a)  #obtain possible next states, probabilities, rewards from environment
#                       print('a,temp_J,npr: ',a,temp_J,nextstates_probs_rews)
                    sum=0.0
                    for npr in nextstates_probs_rews:  #go over all possible next states
                        sum+=npr[1]*(npr[2]+gamma*values[(npr[0][0],npr[0][1])])  #approach optimal value incrementally by using operator T
                    temp_J.append(sum)
                max_J=max(temp_J)  #find maximum value amongst all action's values
                delta=max(delta,abs(values[(x,y)]-max_J))  #maximum value difference 
                extra_info['delta'].append(delta)
                vg[(x,y)],pl[(x,y)]=max_J,possible_actions[temp_J.index(max_J)]  #updating value, policy
        values,policy=vg.copy(),pl.copy()  #.copy() to avoid call by reference
        extra_info['brown_in'].append(values[state_brown_in])
        extra_info['brown_out'].append(values[state_brown_out])
        extra_info['grey_in'].append(values[state_grey_in])
    # End code

    # Do not change the number of output values
    return {"Values": values, "Policy": policy}, extra_info

env = GridEnv_HW2(**base_kwargs)
res, extra_info = value_iteration(env, 0.7)

 # The rounding off is just for making print statement cleaner
print(np.flipud(np.round(res['Values'], decimals=2)))
print(np.flipud(res['Policy']))

def policy_iteration(env, gamma):
    # Initial Values
    values = np.zeros((10, 10))

    # Initial policy
    policy = np.empty((10, 10), object)
    policy[:] = 'N' # Make all the policy values as 'N'

    # Begin code here   
    extra_info = {}  #Put your extra information needed for plots etc in this dictionary
    time_local,time_global,unequal_policy=0,0,0
    extra_info['time_local'],extra_info['time_global'],extra_info['delta'],extra_info['unequal_policy']=[],[],[],[]    
    done=0
    brown_in_loc = np.where(env.grid == 'brown_in')
    brown_out_loc = np.where(env.grid == 'brown_out')
    grey_in_loc = np.where(env.grid == 'grey_in')
    state_brown_in = brown_in_loc[0][0], brown_in_loc[1][0]   
    state_brown_out = brown_out_loc[0][0], brown_out_loc[1][0]   
    state_grey_in = grey_in_loc[0][0], grey_in_loc[1][0]   
    extra_info['brown_in']=[]
    extra_info['brown_out']=[]
    extra_info['grey_in']=[]
    extra_info['values']=[]
    possible_actions=list(env.actions.keys())
    while (done==0):
        time_global+=1
        delta=1e-8
        while (delta>=1e-8):  #repeat until delta reduces less than 1e-8 
            delta=0.0
            time_local+=1
            for x in range(env.grid_size[0]):  #go over all x-coordinates
                for y in range(env.grid_size[1]):  #go over all y-coordinates
                    j=values[(x,y)]
                    action=policy[(x,y)]
                    nextstates_probs_rews=env.get_transition_probabilites_and_reward((x,y),action)  #obtain possible next states, probabilities, rewards from environment
                    sum=0.0
                    for npr in nextstates_probs_rews:  #go over all possible next states
                        sum+=npr[1]*(npr[2]+gamma*values[(npr[0][0],npr[0][1])])  #approach optimal value incrementally by using operator T
                    values[(x,y)]=sum
                    delta=max(delta,abs(values[(x,y)]-j))  #maximum value difference 
                    extra_info['delta'].append(delta)
        extra_info['time_local'].append(time_local)
        time_local=0
        done=1
        for x in range(env.grid_size[0]):  #go over all x-coordinates
            for y in range(env.grid_size[1]):  #go over all y-coordinates
                b=policy[(x,y)]
                temp_J=[]
                for a in possible_actions:  #go over all possible actions
                    nextstates_probs_rews=env.get_transition_probabilites_and_reward((x,y),a)  #obtain possible next states, probabilities, rewards from environment
                    sum=0.0
                    for npr in nextstates_probs_rews:  #go over all possible next states
                        sum+=npr[1]*(npr[2]+gamma*values[(npr[0][0],npr[0][1])])  #approach optimal value incrementally by using operator T
                    temp_J.append(sum)
                policy[(x,y)]=possible_actions[np.argmax(np.array(temp_J))]  #find maximum value amongst all action's values
                if b!=policy[(x,y)]:
                    done=0
                    if len(extra_info['unequal_policy'])<time_global:
                        extra_info['unequal_policy'].append(0)
                    extra_info['unequal_policy'][time_global-1]+=1
        extra_info['time_global'].append(time_global)
        extra_info['brown_in'].append(values[state_brown_in])
        extra_info['brown_out'].append(values[state_brown_out])
        extra_info['grey_in'].append(values[state_grey_in])
        extra_info['values'].append(values.copy())
    # End code

    # Do not change the number of output values
    return {"Values": values, "Policy": policy}, extra_info

env = GridEnv_HW2(**base_kwargs)
res, extra_info = policy_iteration(env, 0.7)

 # The rounding off is just for making print statement cleaner
print(np.flipud(np.round(res['Values'], decimals=2)))
print(np.flipud(res['Policy']))
'''
###used for plotting value convergence curves for value, policy iteration for 3 different states### 
plt.plot(extra_info1['brown_in'],label='value iteration brown-in')  #extra-info1 is from value iterationp  
plt.plot(extra_info2['brown_in'],label='policy iteration brown-in')  #extra-info2 is from policy iteration
plt.title('brown in-value vs iterations')
plt.plot(extra_info1['brown_out'],label='value iteration brown-out')
plt.plot(extra_info2['brown_out'],label='policy iteration brown-out')
plt.title('brown out-value vs iterations')
plt.plot(extra_info1['grey_in'],label='value iteration grey-in')
plt.plot(extra_info2['grey_in'],label='policy iteration grey-in')
plt.legend(loc="lower right")
plt.xlabel('iterations')
plt.ylabel('values')
plt.title('values vs iterations')
'''
'''
###used for plotting rms error over iterations for policy iteration###
opt_val=res['Values']
size_s=opt_val.size
rms_pi=[]
for v in extra_info['values']:
    rms_pi.append(np.sqrt(np.sum(np.square(opt_val-v))/size_s))
plt.plot(rms_pi,label='policy iteration')
plt.legend(loc="upper right")
plt.xlabel('iterations')
plt.ylabel('rms error of values')
plt.title('rms error of values vs iterations')
'''

# The policy mentioned in the pdf to be used for TD lambda, do not modify this
def heuristic_policy(env, state):
    goal = env.goal_location
    dx = goal[0] - state[0]
    dy = goal[1] - state[1]
    if abs(dx) >= abs(dy):
        direction = (np.sign(dx), 0)
    else:
        direction = (0, np.sign(dy))
    for action, dir_val in env.actions.items():
        if dir_val == direction:
            target_action = action
            break
    return target_action

def td_lambda(env, lamda, seeds):
    alpha = 0.5
    gamma = 0.7
    N = len(seeds)
    # Usage of input_policy
    # heuristic_policy(env, state) -> action
    example_action = heuristic_policy(env, (1,2)) # Returns 'N' if goal is (9,9)

    # Example of env.step
    # env.step(state, action) -> Returns next_state, reward, is_terminal

    # Initial values
    values = np.zeros((10, 10))
    es = np.zeros((10,10))

    for episode_idx in range(N):
         # Do not change this else the results will not match due to environment stochas
        np.random.seed(seeds[episode_idx])
        grey_in_loc = np.where(env.grid == 'grey_in')
        state = grey_in_loc[0][0], grey_in_loc[1][0]
        done = False
        extra_info = {'delta':[],'values':[]}  #Put your extra information needed for plots etc in this dictionary
        while not done:
            action = heuristic_policy(env, state)
            ns, rew, is_terminal = env.step(state, action) 
            # env.step is already taken inside the loop for you, 
            # Don't use env.step anywhere else in your code
            # Begin code here
            delta = rew+gamma*values[ns]-values[state]
            extra_info['delta'].append(delta)
            es[state]+=1
            for x in range(env.grid_size[0]):  #go over all x-coordinates
                for y in range(env.grid_size[1]):  #go over all y-coordinates
                    values[(x,y)]+=alpha*delta*es[(x,y)]
                    es[(x,y)]=gamma*lamda*es[(x,y)]
            state=ns
            if is_terminal:  #env._grid_state(state) == 'goal'
                done = True
                possible_actions=list(env.actions.keys())
                policy,policy_index = np.empty((10, 10), object), np.empty((10, 10), object)
                for x in range(env.grid_size[0]):  #go over all x-coordinates
                    for y in range(env.grid_size[1]):  #go over all y-coordinates
                        temp_J=[]
                        for a in possible_actions:  #go over all possible actions
                            nextstates_probs_rews=env.get_transition_probabilites_and_reward((x,y),a)  #obtain possible next states, probabilities, rewards from environment
                            sum=0.0
                            for npr in nextstates_probs_rews:  #go over all possible next states
                                sum+=npr[1]*(npr[2]+gamma*values[(npr[0][0],npr[0][1])])  #approach optimal value incrementally by using operator T
                            temp_J.append(sum)
                        policy[(x,y)]=possible_actions[np.argmax(np.array(temp_J))]  #find maximum value amongst all action's values
                        policy_index[(x,y)]=np.argmax(np.array(temp_J))
                extra_info['policy']=policy
                extra_info['policy_index']=policy_index
            extra_info['values'].append(values.copy())
    # End code

    # Do not change the number of output values
    return {"Values": values}, extra_info

env = GridEnv_HW2(**base_kwargs)
res, extra_info = td_lambda(env, lamda=1, seeds=np.arange(1000))

 # The rounding off is just for making print statement cleaner
print(np.flipud(np.round(res['Values'], decimals=2)))
'''
###used for plotting rms error over different lambda for TD Lambda###
opt_val=res['Values']
size_s=opt_val.size
rms_pi=[]
for v in extra_info['values']:
    rms_pi.append(np.sqrt(np.sum(np.square(opt_val-v))/size_s))
plt.plot(rms_pi,label='td lambda')
plt.legend(loc="upper right")
plt.xlabel('iterations')
plt.ylabel('rms error of values')
plt.title('rms error of values vs iterations')
'''

# This cell is only for your subjective evaluation results, display the results as asked in the pdf
# You can change it as you require, this code should run TD lamdba by default for different values of lambda

lamda_values = np.arange(0, 100+5, 5)/100
td_lamda_results = {}
extra_info = {}
for lamda_idx in range(len(lamda_values)):
    lamda=lamda_values[lamda_idx]
    env = GridEnv_HW2(**base_kwargs)
    td_lamda_results[lamda], extra_info[lamda] = td_lambda(env, lamda,
                                                           seeds=np.arange(1000))
    print('lambda: ',lamda)
    print('value grid result: ',td_lamda_results[lamda]['Values'])
    if lamda:
        print('sum of absolute difference between two consecutive value grids: ',sum(sum(abs(td_lamda_results[lamda_values[lamda_idx-1]]['Values']-td_lamda_results[lamda]['Values']))))
    ax = sns.heatmap(td_lamda_results[lamda]['Values'], linewidth=0.5)
    plt.title('value grid for lambda: %0.2f'%(lamda))
    plt.show()
    print('policy grid result: ',extra_info[lamda]['policy'])
    ax = sns.heatmap(np.array(extra_info[lamda]['policy_index'],dtype=np.int32), linewidth=0.5)
    plt.title('policy grid for lambda: %0.2f'%(lamda))
    plt.show()

def get_results(kwargs):

    gridenv = GridEnv_HW2(**kwargs)

    policy_iteration_results = policy_iteration(gridenv, 0.7)[0]
    value_iteration_results = value_iteration(gridenv, 0.7)[0]
    td_lambda_results = td_lambda(env, 0.5, np.arange(1000))[0]
    
    final_results = {}
    final_results["policy_iteration"] = policy_iteration_results
    final_results["value_iteration"] = value_iteration_results
    final_results["td_lambda"] = td_lambda_results

    return final_results

# Do not edit this cell, generate results with it as is
if not os.path.exists(AICROWD_RESULTS_DIR):
    os.mkdir(AICROWD_RESULTS_DIR)

for params_file in os.listdir(DATASET_DIR):
  kwargs = np.load(os.path.join(DATASET_DIR, params_file), allow_pickle=True).item()
  results = get_results(kwargs)
  idx = params_file.split('_')[-1][:-4]
  np.save(os.path.join(AICROWD_RESULTS_DIR, 'results_' + idx), results)

# Check your score on the given test cases (There are more private test cases not provided)
target_folder = 'targets'
result_folder = AICROWD_RESULTS_DIR

def check_algo_match(results, targets):
    if 'Policy' in results:
        policy_match = results['Policy'] == targets['Policy']
    else:
        policy_match = True
    # Reference https://numpy.org/doc/stable/reference/generated/numpy.allclose.html
    rewards_match = np.allclose(results['Values'], targets['Values'], rtol=3)
    equal = rewards_match and policy_match
    return equal

def check_score(target_folder, result_folder):
    match = []
    for out_file in os.listdir(result_folder):
        res_file = os.path.join(result_folder, out_file)
        results = np.load(res_file, allow_pickle=True).item()
        idx = out_file.split('_')[-1][:-4]  # Extract the file number
        target_file = os.path.join(target_folder, f"targets_{idx}.npy")
        targets = np.load(target_file, allow_pickle=True).item()
        algo_match = []
        for k in targets:
            algo_results = results[k]
            algo_targets = targets[k]
            algo_match.append(check_algo_match(algo_results, algo_targets))
        match.append(np.mean(algo_match))
    return np.mean(match)

if os.path.exists(target_folder):
    print("Shared data Score (normalized to 1):", check_score(target_folder, result_folder))

!DATASET_PATH=$AICROWD_DATASET_PATH aicrowd notebook submit --no-verify -c iit-m-rl-assignment-2-gridworld -a assets