# Choose the greedy action and the Q values
current_f = area.getDeltaF()
a,Q_values = session.run([mainNet.predict,mainNet.Qout],
feed_dict={mainNet.inputs:np.array(current_f).reshape(1,1)})
a = a[0]
# Explore if epsilon parameter agrees
if np.random.rand() < epsilon:
a = np.random.randint(0,2)
# Take the action, modify environment and get the reward
generator = rl.setDiscretePower(a,generator)
area.calculateDeltaF([generator,load])
new_f = area.getDeltaF()
r = rl.getSimpleReward(new_f)
cumm_r += r
# Store the experience and print some data
buffer.add(np.reshape(np.array([current_f,new_f,a,r]),[1,4]))
print("Delta f: ",round(current_f,2)," Action: ",a, " Reward: ",r)
# Update the model each 32 steps with a minibatch of 32
if ((j % 4) == 0) & (len(buffer.buffer) > 32):
miniBatch = buffer.sample(size = 32)
# Run the network for current state and next state
Q_values = session.run(mainNet.Qout,feed_dict={mainNet.inputs:np.reshape(miniBatch[:,0],[32,1])})
Q_mainNet = session.run(mainNet.Qout,feed_dict={mainNet.inputs:np.reshape(miniBatch[:,1],[32,1])})
Q_targetNet = session.run(targetNet.Qout,feed_dict={targetNet.inputs:np.reshape(miniBatch[:,1],[32,1])})
a_max_mainNet = np.argmax(Q_mainNet,axis=1)
current_f = area.getDeltaF()
a, new_state = session.run(
[net.pi_sample, net.rnn_state],
feed_dict={
net.inputs: np.array(current_f).reshape(1, 1),
net.batch_size: 1,
net.trainLength: 1,
net.state_in: state
})
a = a[0, 0] + epsilon * np.random.normal(0.0, 1)
state = new_state
# Take the action, modify environment and get the reward
generator = rl.setContinuousPower(a, generator)
area.calculateDeltaF([generator, load])
r = rl.getSimpleReward(area.getDeltaF())
# Store the rewards and states
cumm_r += r
r_episode.append(r)
f_episode.append(current_f)
a_episode.append(a)
# Print some data to observe the evolution of the system
print("Delta f: ", round(current_f, 2), " Action: ", a,
" Reward: ", r)
# Update epsilon
epsilon = rl.getNewEpsilon(epsilon)
# End episode if delta f is too large