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
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
if done:
new_state = np.zeros(state.shape)
memory.add((state, a, reward, new_state, done)) # Add experience to memory
else:
memory.add((state, a, reward, new_state, done)) # Add experience to memory
state, reward, action, new_state, done = memory.sample()
Q_target = reward + y * np.max(model.predict(new_state)) # Calculate Q-value based on new state
Q_values = model.predict(state)[0] # Get both Q-values for this state
Q_values[a] = Q_target # Update Q-value for the action we took
model.fit(state, Q_values.reshape(-1, 2), epochs=1, verbose=0) # Fit neural network to predict the Q-values
