train_batch = exp_buffer.sample(batch_size)
pred_act, _ = main_qn.predict_act(
np.vstack(train_batch[:, 3]), batch_size, sess)
_, q_vals = target_qn.predict_act(
np.vstack(train_batch[:, 3]), batch_size, sess)
end_multiplier = -(train_batch[:, 4] - 1)
double_q = q_vals[range(batch_size), pred_act]
target_q_val = train_batch[:,
2] + gamma * double_q * end_multiplier
in_frames = np.vstack(train_batch[:, 0])
acts = train_batch[:, 1]
main_qn.update_nn(in_frames, target_q_val, acts,
batch_size, sess, summ_writer,
step_value)
step_value = sess.run(inc_global_step)
s = s1
s_frame = s1_frame
ep_rewards.append(reward)
total_step += 1
if total_step % update_target_step == 0:
sess.run(update_qn_op)
if done:
disc_r = discounted_reward(ep_rewards, gamma)
score = discounted_reward(ep_rewards, 1)
