def __init__(self): self.card_min = 1 # min absolute val of card self.card_max = 10 # max absolute val of card self.dl_values = 10 # possible values for dl in state self.pl_values = 21 # possible values for pl in state self.act_values = len( Actions.get_values()) # number of possible actions
def eps_greedy_choice_linear(self, state, epsilon): Qa = np.zeros(2) # epsilon greedy policy if random.random() > epsilon: for action in Actions.get_values(): phi = self.feature_computation(state, action) Qa[action] = sum(phi*self.theta) a_next = Actions.get_action(np.argmax(Qa)) else: a_next = Actions.hit if random.random()<0.5 else Actions.stick phi = self.feature_computation(state, a_next) my_Qa = sum(phi*self.theta) return [a_next, my_Qa]
def eps_greedy_choice_linear(self, state, epsilon): Qa = np.zeros(2) # epsilon greedy policy if random.random() > epsilon: for action in Actions.get_values(): phi = self.feature_computation(state, action) Qa[action] = sum(phi * self.theta) a_next = Actions.get_action(np.argmax(Qa)) else: a_next = Actions.hit if random.random() < 0.5 else Actions.stick phi = self.feature_computation(state, a_next) my_Qa = sum(phi * self.theta) return [a_next, my_Qa]
def __init__(self): self.card_min = 1 # min absolute val of card self.card_max = 10 # max absolute val of card self.dl_values = 10 # possible values for dl in state self.pl_values = 21 # possible values for pl in state self.act_values = len(Actions.get_values()) # number of possible actions
def TD_control_linear(self, iterations, mlambda, avg_it): self.mlambda = float(mlambda) self.iter = iterations self.method = "Sarsa_control_linear_approx" epsilon = 0.05 alpha = 0.01 l_mse = 0 e_mse = np.zeros((avg_it,self.iter)) monte_carlo_Q = pickle.load(open("Data/Qval_func_1000000_MC_control.pkl", "rb")) n_elements = monte_carlo_Q.shape[0]*monte_carlo_Q.shape[1]*2 for my_it in xrange(avg_it): self.Q = np.zeros((self.env.dl_values, self.env.pl_values, self.env.act_values)) self.LinE = np.zeros(len(self.d_edges)*len(self.p_edges)*2) self.theta = np.random.random(36)*0.2 #self.theta = np.zeros(len(self.d_edges)*len(self.p_edges)*2) count_wins = 0 # Loop over episodes (complete game runs) for episode in xrange(self.iter): self.LinE = np.zeros(36) s = self.env.get_initial_state() if np.random.random() < 1-epsilon: Qa = -100000 a = None for act in Actions.get_values(): phi_curr = self.feature_computation(s,act) Q = sum(self.theta*phi_curr) if Q > Qa: Qa = Q a = act phi = phi_curr else: a = Actions.stick if np.random.random()<0.5 else Actions.hit phi = self.feature_computation(s,a) Qa = sum(self.theta*phi) # Execute until game ends while not s.term: # Accumulating traces self.LinE[phi==1] += 1 # execute action s_next = self.env.step(s, a) # compute delta delta = s_next.rew - sum(self.theta*phi) # choose next action with epsilon greedy policy if np.random.random() < 1-epsilon: Qa = float(-100000) a = None for act in Actions.get_values(): phi_curr = self.feature_computation(s_next,act) Q = sum(self.theta*phi_curr) if Q > Qa: Qa = Q a = act phi = phi_curr else: a = Actions.stick if np.random.random()<0.5 else Actions.hit phi = self.feature_computation(s_next,a) Qa = sum(self.theta*phi) # delta delta += Qa self.theta += alpha*delta*self.LinE self.LinE = self.mlambda*self.LinE # reassign s and a s = s_next #if episode%10000==0: print "Episode: %d, Reward: %d" %(episode, s_next.rew) count_wins = count_wins+1 if s_next.rew==1 else count_wins self.Q = self.deriveQ() e_mse[my_it, episode] = np.sum(np.square(self.Q-monte_carlo_Q))/float(n_elements) print float(count_wins)/self.iter*100 self.Q = self.deriveQ() l_mse += np.sum(np.square(self.Q-monte_carlo_Q)) if mlambda==0 or mlambda==1: plt.plot(e_mse.mean(axis=0)) plt.ylabel('mse vs episodes') plt.show() # Derive value function for d in xrange(self.env.dl_values): for p in xrange(self.env.pl_values): self.V[d,p] = max(self.Q[d, p, :]) #print self.theta return l_mse/float(n_elements)
def TD_control_linear(self, iterations, mlambda, avg_it): self.mlambda = float(mlambda) self.iter = iterations self.method = "Sarsa_control_linear_approx" epsilon = 0.05 alpha = 0.01 l_mse = 0 e_mse = np.zeros((avg_it, self.iter)) monte_carlo_Q = pickle.load( open("Data/Qval_func_1000000_MC_control.pkl", "rb")) n_elements = monte_carlo_Q.shape[0] * monte_carlo_Q.shape[1] * 2 for my_it in xrange(avg_it): self.Q = np.zeros( (self.env.dl_values, self.env.pl_values, self.env.act_values)) self.LinE = np.zeros(len(self.d_edges) * len(self.p_edges) * 2) self.theta = np.random.random(36) * 0.2 #self.theta = np.zeros(len(self.d_edges)*len(self.p_edges)*2) count_wins = 0 # Loop over episodes (complete game runs) for episode in xrange(self.iter): self.LinE = np.zeros(36) s = self.env.get_initial_state() if np.random.random() < 1 - epsilon: Qa = -100000 a = None for act in Actions.get_values(): phi_curr = self.feature_computation(s, act) Q = sum(self.theta * phi_curr) if Q > Qa: Qa = Q a = act phi = phi_curr else: a = Actions.stick if np.random.random( ) < 0.5 else Actions.hit phi = self.feature_computation(s, a) Qa = sum(self.theta * phi) # Execute until game ends while not s.term: # Accumulating traces self.LinE[phi == 1] += 1 # execute action s_next = self.env.step(s, a) # compute delta delta = s_next.rew - sum(self.theta * phi) # choose next action with epsilon greedy policy if np.random.random() < 1 - epsilon: Qa = float(-100000) a = None for act in Actions.get_values(): phi_curr = self.feature_computation(s_next, act) Q = sum(self.theta * phi_curr) if Q > Qa: Qa = Q a = act phi = phi_curr else: a = Actions.stick if np.random.random( ) < 0.5 else Actions.hit phi = self.feature_computation(s_next, a) Qa = sum(self.theta * phi) # delta delta += Qa self.theta += alpha * delta * self.LinE self.LinE = self.mlambda * self.LinE # reassign s and a s = s_next #if episode%10000==0: print "Episode: %d, Reward: %d" %(episode, s_next.rew) count_wins = count_wins + 1 if s_next.rew == 1 else count_wins self.Q = self.deriveQ() e_mse[my_it, episode] = np.sum( np.square(self.Q - monte_carlo_Q)) / float(n_elements) print float(count_wins) / self.iter * 100 self.Q = self.deriveQ() l_mse += np.sum(np.square(self.Q - monte_carlo_Q)) if mlambda == 0 or mlambda == 1: plt.plot(e_mse.mean(axis=0)) plt.ylabel('mse vs episodes') plt.show() # Derive value function for d in xrange(self.env.dl_values): for p in xrange(self.env.pl_values): self.V[d, p] = max(self.Q[d, p, :]) #print self.theta return l_mse / float(n_elements)