add training time to a2c

train_a2c.py

@@ -135,6 +135,8 @@ for episode in range(n_episode):
 
         state = next_state
 
+print(f"Training time for {args.n_episode} episodes: {timer() - start_time}")
+
 # save the model parameters
 torch.save(policy_net.state_dict(), os.path.join(OUT_PATH, 'policy_net{}.pk1'.format(n_episode)))