Reproducing the scores reported by the IQN paper

Thank you for opensourcing such a great code!

I have questions about your IQN implementation, especially on how it can reproduce the scores reported by th paper.

First, your config file https://github.com/google/dopamine/blob/master/dopamine/agents/implicit_quantile/configs/implicit_quantile_icml.gin specifies N=N'=64. How did you choose these values?

Second, can the IQN implementation reproduce the scores reported by the paper? I ran it by myself against six games, but the results do not match the paper.

I used this command:
```
python3 -um dopamine.atari.train '--agent_name=implicit_quantile' '--base_dir=results' '--gin_files=dopamine/agents/implicit_quantile/configs/implicit_quantile_icml.gin' '--gin_bindings=AtariPreprocessing.terminal_on_life_loss=True' "--gin_bindings=Runner.game_name='Breakout'"
```

Here is the tensorboard plot I got:

Seeing Figure 7 of the IQN paper, they report the raw score of 342,016 for Asterix, 42,776 for Beam Rider, 734 for Breakout, 25,750 for Q*Bert, 30,140 for Seaquest, 28,888 for Space Invaders. Have you successfully reproduced scores on the same level? If yes, how? If no, are you aware of any differences of implementation or settings from DeepMind's?