diff --git a/Documentos/TFG_Machine_Learning/Reinforce_Learning.py b/Documentos/TFG_Machine_Learning/Reinforce_Learning.py
new file mode 100644
index 0000000..2c31b41
--- /dev/null
+++ b/Documentos/TFG_Machine_Learning/Reinforce_Learning.py
@@ -0,0 +1,620 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Feb  6 19:02:32 2024
+
+@author: marta
+"""
+
+import numpy as np
+import plotly.graph_objects as go
+from tqdm.notebook import tqdm
+import plotly.express as px
+import matplotlib as mpl
+mpl.rcParams['figure.dpi'] = 300
+import matplotlib.pyplot as plt
+import seaborn as sns
+import os
+from wand.image import Image as WImage
+# sns.set(palette="husl",font_scale=1)
+# %config InlineBackend.figure_format = 'retina'
+import copy
+np.random.seed(4032)
+#%load_ext line_profiler
+
+
+
+#Define constants
+
+#L = 2*np.pi # periodic domain size
+L=10
+# define boundaries of simulation box
+x0 = 0
+x1 = L
+z0 = 0
+z1 = L
+
+# define reinforcement learning problem
+N_states = 4 # number of states - one for each coarse-grained degree of vorticity
+N_actions = 2 # number of actions - one for each coarse-grained swimming direction
+
+# numerical parameters
+dt = 0.0001 # timestep size
+
+
+
+#Utility functions
+
+def moving_average(a, n=3) :
+    ret = np.cumsum(a, dtype=float)
+    ret[n:] = ret[n:] - ret[:-n]
+    return ret[n - 1:] / n
+
+# Runga-Kutta 4(5) integration for one step
+    # see https://stackoverflow.com/questions/54494770/how-to-set-fixed-step-size-with-scipy-integrate
+def DoPri45Step(f,t,x,h):
+
+    k1 = f(t,x)
+    k2 = f(t + 1./5*h, x + h*(1./5*k1) )
+    k3 = f(t + 3./10*h, x + h*(3./40*k1 + 9./40*k2) )
+    k4 = f(t + 4./5*h, x + h*(44./45*k1 - 56./15*k2 + 32./9*k3) )
+    k5 = f(t + 8./9*h, x + h*(19372./6561*k1 - 25360./2187*k2 + 64448./6561*k3 - 212./729*k4) )
+    k6 = f(t + h, x + h*(9017./3168*k1 - 355./33*k2 + 46732./5247*k3 + 49./176*k4 - 5103./18656*k5) )
+
+    v5 = 35./384*k1 + 500./1113*k3 + 125./192*k4 - 2187./6784*k5 + 11./84*k6
+    k7 = f(t + h, x + h*v5)
+    v4 = 5179./57600*k1 + 7571./16695*k3 + 393./640*k4 - 92097./339200*k5 + 187./2100*k6 + 1./40*k7;
+
+    return v4,v5
+
+
+
+#Define useful data structures
+#Define a dictionary of the possible states and their assigned indices
+
+distance_states = ["ri", "rni"] #ri es rij<rct y rni es rij>rct
+frecuency_states = ["wo", "wh"] #wo es w<wc y wh es w>wc
+product_states = [(x,y) for x in distance_states for y in frecuency_states]  # all possible states
+state_lookup_table = {product_states[i]:i for i in range(len(product_states))} # returns index of given state
+# print(product_states) # to view mapping
+
+#Define an agent class for reinforcement learning
+
+class Agent:
+    def __init__(self, Ns):
+        self.r = np.zeros(Ns) # reward for each stage
+        self.t = 0            # time
+
+    # calculate reward given from entering a new state after a selected action is undertaken
+    def calc_reward(self):
+        # enforce implementation by subclass
+        if self.__class__ == AbstractClass:
+                raise NotImplementedError
+
+    def update_state(self):
+        # enforce implementation by subclass
+        if self.__class__ == AbstractClass:
+                raise NotImplementedError
+
+    def take_random_action(self):
+        # enforce implementation by subclass
+        if self.__class__ == AbstractClass:
+                raise NotImplementedError
+
+    def take_greedy_action(self, Q):
+        # enforce implementation by subclass
+        if self.__class__ == AbstractClass:
+                raise NotImplementedError
+                
+#Define swimmer class derived from agent
+
+class Swimmer(Agent):
+    def __init__(self, Ns, ni, sigma):
+        # call init for superclass
+        super().__init__(Ns)
+        
+        self.ni = ni
+        self.sigma = sigma
+
+        # local position within the periodic box. X = [x, z]^T with 0 <= x < 2 pi and 0 <= z < 2 pi
+        self.X = np.array([np.random.uniform(0, L), np.random.uniform(0, L), 0])
+        
+        # absolute position. -inf. <= x_total < inf. and -inf. <= z_total < inf.
+        self.X_total = self.X
+
+        # particle orientation
+        self.theta = np.random.uniform(0, 2*np.pi) # polar angle theta in the x-z plane
+        self.p = np.array([np.cos(self.theta), np.sin(self.theta)]) # p = [px, pz]^T
+
+        # translational and rotational velocity
+        self.U = np.zeros(3, float)
+        self.W = np.array([0., 0., 1.]) #Velocidad angular aleatoria
+        
+        #distancia entre el swimmer y el obstáculo
+        self.R=np.random.uniform(0, 2.5, 1)
+        print(self.R)
+        
+        # preferred swimming direction (equal to [1,0], [0,1], [-1,0], or [0,-1])
+        self.ka = np.array([0,1])
+
+        # history of local and global position. Only store information for this episode.
+        self.history_X = [self.X]
+        self.history_X_total = [self.X_total]
+
+        # local vorticity at the current location
+        _, _, self.w = tgv(self.X[0], self.X[1])
+        
+
+        # update coarse-grained state
+        self.update_state()
+
+        #obstáculos
+        self.obstacles= self.generate_obstacles()
+        
+        
+        #distancia entre el swimmer y el obstáculo
+        #self.R = [np.linalg.norm(self.X[:2] - np.array([obstacle_x, obstacle_y])) for obstacle_x, obstacle_y in zip(self.obstacles[::2], self.obstacles[1::2])]
+        
+
+    def generate_obstacles(self):
+        obstacles=[] #el numero de obstáculos será 10*10
+        cell_spacing= L/4
+
+        for i in range(4):
+          for j in range(4):
+            obstacle_x= i*cell_spacing
+            obstacle_y= j*cell_spacing
+            obstacles.append(obstacle_x)
+            obstacles.append(obstacle_y)
+
+        return obstacles
+   
+    
+       
+    def interaction_with_obstacles(self,obstacles,kappa,alpha,beta,gamma,Pe,dt):
+      F= np.array([0.,0.,0.])
+      for i in range(len(obstacles)//2):
+        #F1
+        obstacle_position = np.array([obstacles[2*i],obstacles[2*i+1], 0])
+        r=self.X - obstacle_position
+        r_norm=np.linalg.norm(r)
+        Re= (.5*self.sigma)**2*np.linalg.norm(self.W)/self.ni
+        S=1/(1+np.exp(-kappa*((Re/r_norm**3)-Re)))
+        F1=alpha*(Re/r_norm**3)*np.cross(self.U,self.W)*S
+
+        #F2
+        F2=beta*np.cross(self.W,r)/r_norm**3
+
+        #F de atracción
+        F_attr= gamma*(np.exp(-r_norm/kappa)/r_norm**2)*(kappa+r_norm)*r
+
+
+        #Fuerza total
+        F+=F1+F2+F_attr
+
+      xi=np.random.normal(0,1, size=2) #vector de números aleatorios generados a partir de una distribución normal estándar con dos componentes, xi creo que es un vector de ruido estocástico (modela el ruido térmico)
+      dr_therm = np.sqrt(2*self.sigma**2*dt/Pe)*xi
+
+      dr = F[:-1]*dt + dr_therm
+
+      #actualizamos la posición del spinner
+      self.X[:-1] += dr
+      self.U = np.array([dr[0]/dt, dr[1]/dt, 0])
+      
+      all_dists = np.empty(len(obstacles)//2)
+      
+      for i in range(len(obstacles)//2):
+          all_dists[i] = np.linalg.norm(self.X-np.array([obstacles[2*i],obstacles[2*i+1], 0]))
+
+      self.R = np.amin(all_dists)      
+      #comprobamos que el spinner siga dentro del box periódico
+      self.check_in_box()
+
+
+    def reinitialize(self):
+        self.X = np.array([np.random.uniform(0, L), np.random.uniform(0, L)])
+        self.X_total = self.X
+
+        self.theta = np.random.uniform(0, 2*np.pi) # polar angle theta in the x-z plane
+        self.p = np.array([np.cos(self.theta), np.sin(self.theta)]) # p = [px, pz]^T # orientación del nadador
+
+        self.U = np.zeros(2)
+        self.W = np.array([0, 0, 1])
+
+        self.ka = np.array([0,1])
+
+        self.history_X = [self.X]
+        self.history_X_total = [self.X_total]
+        
+        self.R=np.random.uniform(0, 2.5)
+        
+        self.t = 0
+
+    def update_kinematics(self, Φ, Ψ, D0 = 0, Dr = 0, int_method = "euler"): # Actualiza la posición y orientación del nadador según un método de integración especificado.
+        if int_method == "rk45":
+            y0 = np.concatenate((self.X,self.p))
+            _, v5 = DoPri45Step(self.calc_velocity_rk45,self.t,y0,dt)
+            y = y0 + dt*v5
+            self.X = y[:2]
+            self.p = y[2:]
+            dx = self.X - self.history_X[-1]
+            self.X_total = self.X_total + dx
+
+            # check if still in the periodic box
+            self.check_in_box()
+
+            # ensure the vector p has unit length
+            self.p /= (self.p[0]**2 + self.p[1]**2)**(1/2)
+
+            # update polar angle
+            x = self.p[0]
+            yy = self.p[1]
+            self.theta = np.arctan2(yy,x) if yy >= 0 else (np.arctan2(yy,x) + 2*np.pi)
+
+            # store positions
+            self.history_X.append(self.X)
+            self.history_X_total.append(self.X_total)
+
+        elif int_method == "euler":
+            # calculate new translational and rotational velocity
+            self.calc_velocity(Φ, Ψ)
+
+            self.update_position(int_method, D0)
+            self.update_orientation(int_method, Dr)
+        else:
+            raise Exception("Integration method must be 'Euler' or 'rk45'")
+
+        self.t = self.t + dt
+
+    def calc_velocity_rk45(self, t, y): #calcula la velocidad del nadador en un determinado tiempo 't' y estado 'y' utilizando Rk45
+        x = y[0]
+        z = y[1]
+        px = y[2]
+        pz = y[3]
+        ux, uz, self.w = tgv(x, z) #tgv proporciona velocidades de flujo en la posición (x,z), w es la vorticidad
+
+        #cálculo de las velocidades translacionales
+        U0 = ux + Φ*px #ux y uz son las velocidades del flujo
+        U1 = uz + Φ*pz
+
+       #cálculo de las velocidades rotacionales
+        ka_dot_p = self.ka[0]*px + self.ka[1]*pz #alineación del vector de nado preferido con la dirección del nadador
+        W0 = 1/2/Ψ*(self.ka[0] - ka_dot_p*px) + 1/2*pz*self.w
+        W1 = 1/2/Ψ*(self.ka[1] - ka_dot_p*pz) + 1/2*-px*self.w
+
+        return np.array([U0, U1, W0, W1])
+
+
+    def update_position(self, int_method, D0): #D0 representa la difusión
+        # use explicit euler to update
+        dx = dt*self.U
+        if D0 > 0: dx = dx + np.sqrt(2*D0*dt)*np.random.normal(size=2) #posible efecto de la difusión browniana
+        self.X = self.X + dx
+        self.X_total = self.X_total + dx
+
+        # check if still in the periodic box
+        self.check_in_box()
+
+        # store positions
+        self.history_X.append(self.X)
+        self.history_X_total.append(self.X_total)
+
+
+    def update_orientation(self, int_method, Dr):
+        self.p = self.p + dt*self.W #W velocidad angular
+
+        # ensure the vector p has unit length
+        self.p /= (self.p[0]**2 + self.p[1]**2)**(1/2)
+
+        # if rotational diffusion is present
+        if Dr > 0: #Dr representa difucion rotacional
+            px = self.p[0]
+            pz = self.p[1]
+            cross = px*pz
+            A = np.array([[1-px**2, -cross], [-cross, 1-pz**2]]) #A es una matriz
+            v = np.sqrt(2*Dr*dt)*np.random.normal(size=2) #v es un vector de valores aleatorios
+            self.p[0] = self.p[0] + A[0,0]*v[0] + A[0,1]*v[1] #Se calcula un cambio aleatorio en la orientación usando A y v
+            self.p[1] = self.p[1] + A[1,0]*v[0] + A[1,1]*v[1]
+            self.p /= (self.p[0]**2 + self.p[1]**2)**(1/2)
+
+        # update polar angle
+        x = self.p[0]
+        y = self.p[1]
+        self.theta = np.arctan2(y,x) if y >= 0 else (np.arctan2(y,x) + 2*np.pi)
+
+
+
+    def calc_velocity(self, Φ, Ψ):
+        ux, uz, self.w = tgv(self.X[0], self.X[1])
+
+        # careful - computing in the following way is significantly slower: self.U = np.array(ux, uz) + Φ*self.p
+        self.U[0] = ux + Φ*self.p[0]
+        self.U[1] = uz + Φ*self.p[1]
+
+        px = self.p[0]
+        pz = self.p[1]
+        ka_dot_p = self.ka[0]*px + self.ka[1]*pz
+        self.W[0] = 1/2/Ψ*(self.ka[0] - ka_dot_p*px) + 1/2*pz*self.w
+        self.W[1] = 1/2/Ψ*(self.ka[1] - ka_dot_p*pz) + 1/2*-px*self.w
+
+
+    def check_in_box(self): # Este método verifica si el nadador todavía está dentro del cuadro periódico
+        if self.X[0] < x0:
+            self.X[0] += L
+        elif self.X[0] > x1:
+            self.X[0] -= L
+        if self.X[1] < z0:
+            self.X[1] += L
+        elif self.X[1] > z1:
+            self.X[1] -= L
+
+    def calc_reward(self, n):
+        self.r[n] = self.history_X_total[-1][1]-self.history_X_total[-2][1]
+
+    def update_state(self):
+        
+        #self.distance_obstacles()
+        #componente z de la velocidad angular
+        W_z = self.W[2]
+        if W_z <= 0.175*self.ni/(.5*self.sigma*self.sigma):
+            W_state = "wo"
+        elif W_z > 0.175*self.ni/(.5*self.sigma*self.sigma):
+            W_state = "wh"
+        else:
+            raise Exception("Invalid value of w detected: ", W_z)
+        
+        if self.R <= 1.25:
+            R_state = "ri"
+        elif self.R > 1.25:
+            R_state = "rni"
+        else:
+            raise Exception ("Invalid value of r detected: ", self.R)
+       
+
+        self.my_state = (R_state, W_state)
+
+    def take_greedy_action(self, Q):
+        state_index = state_lookup_table[self.my_state]
+        action_index = np.argmax(Q[state_index])  # find largest entry in this row of Q (i.e. this state)
+        Wc=0.175*self.ni/(.5*self.sigma*self.sigma)
+        if action_index == 0:   # aumenta 1/8W
+            self.W[2] += 1./8*Wc 
+        elif action_index == 1:                   # disminuye 1/8W
+            self.W[2] -= 1./8*Wc 
+        else:
+            raise Exception ("Action index out of bounds: ", action_index)
+        return action_index
+
+    def take_random_action(self):
+        action_index = np.random.randint(0, 2, 1)
+        Wc=0.175*self.ni/(.5*self.sigma*self.sigma)
+        if action_index == 0:   # aumenta 1/8W
+            self.W[2] += 1./8*Wc 
+        else:                   # disminuye 1/8W
+            self.W[2] -= 1./8*Wc 
+        return action_index
+    
+ 
+#Define Taylor-Green vortex
+
+# given position, return local velocity and vorticity
+def tgv(x, z):
+    ux = -1/2*np.cos(x)*np.sin(z)
+    uz = 1/2*np.sin(x)*np.cos(z)
+    w = -np.cos(x)*np.cos(z)
+    return ux, uz, w
+
+def training(alpha0, Φ, Ψ, Ns=4000, Ne=5000, gamma=0.999, eps0=0.0, D0=0, Dr=0, n_updates=1000, \
+             RIC=False, method="Qlearning", lr_decay=None, omega=0.85, eps_decay=False, Qin=None):
+    # n_updates - how often to plot the trajectory undertaken by the particle during the learning process
+    # Ne - number of episodes
+    # Ns - number of steps in an episode
+    # alpha0 - learning rate (or starting learning rate when employing LR decay)
+    # gamma - discount factor, i.e. how much we weigh future to present rewards. Close to 0 = myopic view.
+    # eps0 - fraction of the time we allow for exploration in selecting the following action. 0 = always greedy.
+    # D0 - translational diffusivity
+    # Dr - rotational diffusivity
+    # RIC - Reset of Initial Conditions. First time a state-action pair is encountered, set Q[s,a] = reward
+    # method - choose from Q-learning, Double Q-learning (, or Expected SARSA
+    # lr_decay - whether or not to use learning rate decay. Options are none, or polynomial (lr=1/#(s,a)**omega)
+    # omega - exponent used in lr_decay: lr = 1/#(s,a)**omega
+    # eps_decay - whether or not to decay epsilon linearly: eff_eps = eps0/k for the k-th step
+    # Qin - initial Q matrix. Useful for testing performance after an extensive exploration phase.
+
+    # if using the expected SARSA method, turn on epsilon decay since eps = 0 is simply Q-learning anyway
+    if method=="expSARSA":
+        eps_decay = True
+        if eps0 == 0: eps0 = 1
+
+    # Total reward for each episode
+    hist_R_tot_smart = np.zeros(Ne)
+    hist_R_tot_naive= np.zeros(Ne)
+
+    # learning gain per episode
+    Σ = np.zeros(Ne)
+
+    smart_stored_histories = []       # store position = f(t) every so often for an episode (smart particles)
+    naive_stored_histories = []       # store position = f(t) every so often for an episode (naive particles)
+
+    # number of times each state-action pair has been explored
+    state_action_counter = np.zeros((N_states,N_actions))
+
+    # initialize a naive and a smart gyrotactic particle
+    naive = Swimmer(Ns)
+    smart = Swimmer(Ns)
+
+    # initialize Q matrix to large value
+    if method=="doubleQ":
+        Q1 = L*Ns*np.ones((4, 2))
+        Q2 = L*Ns*np.ones((4, 2))
+    else:
+        Q = L*Ns*np.ones((4, 2))   # 4 states, 2 possible actions. Each column is an action, w.
+
+    if Qin is not None: Q = Qin
+
+    # store average Q for each episode to track convergence
+    avg_Q_history = np.zeros((Ne,4,2))
+
+    # store initial position and orientation for each episode
+    initial_coords = np.zeros((Ne,3))
+
+    # iterate over episodes
+    k = 0
+    for ep in tqdm(range(Ne)):
+
+        # assign random orientation and position
+        smart.reinitialize()
+        naive.reinitialize()
+        naive = copy.deepcopy(smart) # have naive and smart share initial conditions for visualization purposes
+
+        # store initialization
+        initial_coords[ep,0:2] = smart.X
+        initial_coords[ep,2] = smart.theta
+
+        # save selected actions and particle orientation for last episodes
+        if ep == Ne - 1:
+            chosen_actions = np.zeros(Ns)
+            theta_history = np.zeros(Ns)
+
+        # iterate over stages within an episode
+        for stage in range(Ns):
+
+            # select an action eps-greedily. Note naive never changes its action/strategy (i.e. trying to swim up)
+            Qinput = Q1 + Q2 if method=="doubleQ" else Q
+            k = k + 1 # k-th update
+
+            eff_eps = eps0/k**omega if eps_decay else eps0 # decrease amount of exploration as time proceeds
+            if np.random.uniform(0, 1) < eff_eps:
+                action = smart.take_random_action()
+            else:
+                action = smart.take_greedy_action(Qinput)
+
+            # record action and orientation on last episode
+            if ep == Ne - 1:
+                chosen_actions[stage] = action
+                theta_history[stage] = smart.theta
+
+            # record index of the prior state
+            old_s = state_lookup_table[smart.my_state]
+
+            # given selected action, update the state
+            naive.update_kinematics(Φ, Ψ, D0, Dr)
+            smart.update_kinematics(Φ, Ψ, D0, Dr)
+            smart.update_state()      # only need to update smart particle since naive has ka = [0, 1] always
+
+            # calculate reward based on new state
+            naive.calc_reward(stage)
+            smart.calc_reward(stage)
+
+            new_s = state_lookup_table[smart.my_state]
+            state_action_counter[new_s,action] += 1
+
+            # employ learning rate decay if applicable
+            alpha = alpha0/(1+state_action_counter[old_s,action])**omega if lr_decay else alpha0
+
+            # update Q matrix
+            if method=="doubleQ":
+                if np.random.uniform(0, 1) < 0.5: # update Q1
+                    if Q1[old_s, action] == L*Ns and RIC==True: # apply Reset of Initial Conditions (RIC)
+                        Q1[old_s, action] = smart.r[stage]
+                    else:
+                        Q1[old_s, action] = Q1[old_s, action] + alpha*(smart.r[stage] + \
+                                gamma*np.max(Q2[new_s,:])-Q1[old_s,action])
+                else: # update Q2
+                    if Q2[old_s, action] == L*Ns and RIC==True:
+                        Q2[old_s, action] = smart.r[stage]
+                    else:
+                        Q2[old_s, action] = Q2[old_s, action] + alpha*(smart.r[stage] + \
+                                gamma*np.max(Q1[new_s,:])-Q2[old_s,action])
+            if method=="expSARSA":
+                # calculate V, the expected Q value for the next state-actio pair
+                V = 0
+                greedy_action = np.argmax(Q[new_s]) # would-be greedy action for new state
+                for new_action in range(N_actions):
+                    pi = (1 - eff_eps) + eff_eps/N_actions if new_action == greedy_action else eff_eps/N_actions
+                    V = V + pi*Q[new_s, new_action]
+
+                if Q[old_s, action] == L*Ns and RIC==True:
+                    Q[old_s, action] = smart.r[stage]
+                else:
+                    Q[old_s, action] = Q[old_s, action] + alpha*(smart.r[stage] + gamma*V - Q[old_s,action])
+            else:
+                if Q[old_s, action] == L*Ns and RIC==True:
+                    Q[old_s, action] = smart.r[stage]
+                else:
+                    Q[old_s, action] = Q[old_s, action] + alpha*(smart.r[stage] + \
+                            gamma*np.max(Q[new_s,:])-Q[old_s,action])
+
+            # store average Q for each episode to track convergence
+            avg_Q_history[ep] = avg_Q_history[ep] + Q1 + Q2 if method=="doubleQ" else avg_Q_history[ep] + Q
+
+        avg_Q_history[ep] = avg_Q_history[ep]/Ns
+
+
+        # calculate Rtot for this episode
+        R_tot_naive = np.sum(naive.r)
+        R_tot_smart = np.sum(smart.r)
+
+        # calculate learning gain for this episode
+        Σ[ep] = R_tot_smart/R_tot_naive - 1
+        hist_R_tot_smart[ep] = R_tot_smart
+        hist_R_tot_naive[ep] = R_tot_naive
+
+        # plot trajectory every so often
+        if ep%n_updates==0 or ep==Ne-1:
+            smart_history_X_total = np.array(smart.history_X_total)
+            smart_stored_histories.append((ep,smart_history_X_total))
+            naive_history_X_total = np.array(naive.history_X_total)
+            naive_stored_histories.append((ep,naive_history_X_total))
+
+        # save optimal policy
+        if ep==Ne-1:
+            filename = "Policies/Q_alpha_" + str(alpha).replace(".","d") + "_Ns_" + str(Ns) + "_Ne_" + str(Ne) + \
+                    "_Φ_" + str(Φ).replace(".","d") + "_Ψ_" + str(Ψ).replace(".","d") + "_eps_" \
+                    + str(eff_eps).replace(".","d") + "_epsdecay_" + str(eps_decay)
+            if lr_decay: filename = filename + "_omega_" + str(omega)
+            if method=="doubleQ": filename = filename + "_" + str(method)
+            if RIC: filename = filename + "_RIC_" + str(RIC)
+            Qout = Q1 + Q2 if method=="doubleQ" else Q
+            np.save(filename, Qout)
+
+    return Qout, Σ, smart, naive, hist_R_tot_smart, hist_R_tot_naive, smart_stored_histories, naive_stored_histories, \
+        state_action_counter, chosen_actions, avg_Q_history, initial_coords, theta_history
+#Plot
+
+Q = np.random.rand(4, 2)
+
+#Q = np.array([[1, 0],
+              #[2, 1],
+              #[0, 0],
+              #[1, 2]])
+
+print(Q)
+
+Ns = 5000
+spinner = Swimmer(Ns, 1, 1)
+traj = []
+obstacles = spinner.generate_obstacles()
+for i in range(Ns):
+  spinner.interaction_with_obstacles(obstacles, 2.5, 1, 1., 2.5e-4, 10000, 0.001)
+  traj.append(spinner.X[0])
+  traj.append(spinner.X[1])
+
+  action_index = spinner.take_greedy_action(Q)
+  
+
+  spinner.update_state()
+  
+  #print("Mi estado", spinner.my_state)
+  #print("Valor de Wz después de tomar la acción:", spinner.W[2])
+
+fig, ax= plt.subplots(1,1)
+ax.plot(traj[::2], traj[1::2], '.')
+ax.plot(obstacles[::2], obstacles[1::2], '.')
+ax.set_aspect('equal')
+print(obstacles[::2])
+plt.show()
+
+
+#comprobación de que W va cambiando
+