HUAWEI（华为） atlas 200 DK环境配置方法、网络配置、例程速跑及强化学习示例昇腾代码仓使用MobaXterm

import numpy as np # R matrix R = np.matrix([ [-1,-1,-1,-1,0,-1], [-1,-1,-1,0,-1,100], [-1,-1,-1,0,-1,-1], [-1,0,0,-1,0,-1], [-1,0,0,-1,-1,100], [-1,0,-1,-1,0,100] ]) # Q matrix Q = np.matrix(np.zeros([6,6])) # Gamma (learning parameter). gamma = 0.8 # Initial state. (Usually to be chosen at random) initial_state = 1 # This function returns all available actions in the state given as an argument def available_actions(state): current_state_row = R[state,] av_act = np.where(current_state_row >= 0)[1] return av_act # Get available actions in the current state available_act = available_actions(initial_state) # This function chooses at random which action to be performed within the range # of all the available actions. def sample_next_action(available_actions_range): next_action = int(np.random.choice(available_act,1)) return next_action # Sample next action to be performed action = sample_next_action(available_act) # This function updates the Q matrix according to the path selected and the Q # learning algorithm def update(current_state, action, gamma): max_index = np.where(Q[action,] == np.max(Q[action,]))[1] if max_index.shape[0] > 1: max_index = int(np.random.choice(max_index, size = 1)) else: max_index = int(max_index) max_value = Q[action, max_index] # Q learning formula Q[current_state, action] = R[current_state, action] + gamma * max_value # Update Q matrix update(initial_state,action,gamma) #------------------------------------------------------------------------------- # Training # Train over 10 000 iterations. (Re-iterate the process above). for i in range(10000): current_state = np.random.randint(0, int(Q.shape[0])) available_act = available_actions(current_state) action = sample_next_action(available_act) update(current_state,action,gamma) # Normalize the "trained" Q matrix print("Trained Q matrix:") print(Q/np.max(Q)*100) #------------------------------------------------------------------------------- # Testing # Goal state = 5 # Best sequence path starting from 2 -> 2, 3, 1, 5 current_state = 2 steps = [current_state] while current_state != 5: next_step_index = np.where(Q[current_state,] == np.max(Q[current_state,]))[1] if next_step_index.shape[0] > 1: next_step_index = int(np.random.choice(next_step_index, size = 1)) else: next_step_index = int(next_step_index) steps.append(next_step_index) current_state = next_step_index # Print selected sequence of steps print("Selected path:") print(steps)