loss = reward - np.amax(Q_out)
if not done:
new_state = prepare_state(new_state)
Q_out = Q(FloatTensor(new_state)).to('cpu').detach().numpy()[0]
Q_targ_out = Q_targ(
FloatTensor(new_state)).to('cpu').detach().numpy()[0]
loss += gamma * Q_targ_out[np.argmax(Q_out)]
loss = abs(loss)
#print(curr_state.shape)
replay_mem.add_element((curr_state, action, reward, new_state),
loss)
curr_state = new_state
#
#Learning
#
sarses = replay_mem.get_batch(batch_size)
#Targets
Q_true = []
#print()
#print('+++++++++++++++++++++++++++')
