A2C on Pong results

To start the training, run 02_pong_a2c.py with the --cuda and -n options (which provides a name for the run for TensorBoard):

      rl_book_samples/Chapter10$ ./02_pong_a2c.py --cuda -n t2
      AtariA2C (
         (conv): Sequential (
           (0): Conv2d(4, 32, kernel_size=(8, 8), stride=(4, 4))
           (1): ReLU ()
           (2): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2))
           (3): ReLU ()
           (4): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))
           (5): ReLU ()
         )
         (policy): Sequential (
           (0): Linear (3136 -> 512)
           (1): ReLU ()
           (2): Linear (512 -> 6)
         )
         (value): Sequential (
           (0): Linear (3136 -> 512)
           (1): ReLU ()
           (2): Linear (512 -> 1)
         )
      )
      37799: done 1 games, mean reward -21.000, speed 722.89 f/s
      39065: done 2 games, mean reward -21.000, speed 749.92 f/s
      39076: done 3 games, mean...