def pretrain():
sim = Simulation(nodes)
state = sim.get_state()
for i in range(pretrain_length):
a = np.random.randint(0, len(actions)) # Random action
new_state, reward, done = sim.step(actions[a])
if done:
# We finished the episode
new_state = np.zeros(state.shape)
memory.add((state, a, reward, new_state, done)) # Add experience to memory
sim = Simulation(nodes) # Start a new episode
state = sim.get_state() # First we need a state
else:
memory.add((state, a, reward, new_state, done)) # Add experience to memory
state = new_state # Our state is now the next_state
model.add(Dense(5, activation='sigmoid'))
model.add(Dense(len(actions), activation='linear'))
model.compile(loss='mse', optimizer='adam', metrics=['mae'])
# Q-learning
num_episodes = 1000
y = 0.95
eps = 0.5 # Exploration rate
r_avg_list = []
for i in range(num_episodes):
if i % 100 == 0:
print("Episode {} of {}".format(i + 1, num_episodes))
done = False
r_sum = 0
sim = Simulation(nodes)
state = sim.get_state() # Initial state
iteration = 0
while not done:
eps = 1/math.sqrt(iteration + 1) # Gradually decrease exploration rate
if np.random.random() < eps:
a = np.random.randint(0, len(actions)) # Explore by picking a random action
else:
a = np.argmax(model.predict(state)) # Use network to predict which action to take
action = actions[a]
#print(action)
new_state, reward, done = sim.step(action) # Use selected action to update the environment
