Environment Solved

README.md

@@ -53,4 +53,11 @@ python -m sf_examples.atari.train_atari --algo=APPO --env=atari_atlantis --train
 ```
 
 Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
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+## SOTA Performance
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+This model as with all the others was trained at 10 million steps to create a baseline. Interestingly, in this environment, it reaches SOTA performance at even this level suggesting that the Atlantis game is pretty easy to beat.
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+For more information on this environment see: https://www.endtoend.ai/envs/gym/atari/atlantis/. Because rewards are plentiful and the Gorgons have to pass 4 times to reach attack range the environment is relatively easy to reach SOTA on.
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+I have now compared this with the performance of the TQC, SAC and the DQN models which all underperformed PPO. I now consider this atari environment solved.