update more gym models

examples/DisturbLabel/README.md

@@ -15,8 +15,8 @@ Experiements were repeated 15 times for p=0, 10 times for p=0.02 & 0.05, and 5 t
 of p. All experiements run for 100 epochs, with lr decay, which are enough for them to converge.
 
 I suppose the disturb method works as a random noise that could prevent SGD from getting stuck, if
-training data are too easy or too few.
-It didn't work for harder problems such as SVHN:
+training data are too easy to fit or too few.
+The method didn't work for slightly harder problems such as SVHN:
 
 ![svhn](svhn.png)
 

examples/OpenAIGym/README.md

@@ -15,20 +15,35 @@ Models are available for the following gym atari environments (click links for v
 + [Asteroids-v0](https://gym.openai.com/evaluations/eval_8eHKsRL4RzuZEq9AOLZA)
 + [Atlantis-v0](https://gym.openai.com/evaluations/eval_Z1B3d7A1QCaQk1HpO1Rg)
 + [BattleZone-v0](https://gym.openai.com/evaluations/eval_SoLit2bR1qmFoC0AsJF6Q)
++ [BankHeist-v0](https://gym.openai.com/evaluations/eval_hifoaxFTIuLlPd38BjnOw)
 + [BeamRider-v0](https://gym.openai.com/evaluations/eval_KuOYumrjQjixwL0spG0iCA)
 + [Breakout-v0](https://gym.openai.com/evaluations/eval_L55gczPrQJamMGihq9tzA)
++ [Carnival-v0](https://gym.openai.com/evaluations/eval_xJSOlo2lSWaH1wHEOX5vw)
++ [ChopperCommand-v0](https://gym.openai.com/evaluations/eval_tYVKyh7wQieRIKgEvVaCuw)
 + [CrazyClimber-v0](https://gym.openai.com/evaluations/eval_bKeBg0QwSgOm6A0I0wDhSw)
 + [DemonAttack-v0](https://gym.openai.com/evaluations/eval_tt21vVaRCKYzWFcg1Kw)
 + [DoubleDunk-v0](https://gym.openai.com/evaluations/eval_FI1GpF4TlCuf29KccTpQ)
 + [ElevatorAction-v0](https://gym.openai.com/evaluations/eval_SqeAouMvR0icRivx2xprZg)
 + [FishingDerby-v0](https://gym.openai.com/evaluations/eval_pPLCnFXsTVaayrIboDOs0g)
-+ [JourneyEscape](https://gym.openai.com/evaluations/eval_S9nQuXLRSu7S5x21Ay6AA)
++ [Gravitar-v0](https://gym.openai.com/evaluations/eval_QudrLdVmTpK9HF5juaZr0w)
++ [IceHockey-v0](https://gym.openai.com/evaluations/eval_8oWCTwwGS7OUTTGRwBPQkQ)
++ [JourneyEscape-v0](https://gym.openai.com/evaluations/eval_S9nQuXLRSu7S5x21Ay6AA)
++ [Krull-v0](https://gym.openai.com/evaluations/eval_dfOS2WzhTh6sn1FuPS9HA)
++ [KungFuMaster-v0](https://gym.openai.com/evaluations/eval_vNWDShYTRC0MhfIybeUYg)
++ [MsPacman-v0](https://gym.openai.com/evaluations/eval_kpL9bSsS4GXsYb9HuEfew)
++ [Pooyan-v0](https://gym.openai.com/evaluations/eval_UXFVI34MSAuNTtjZcK8N0A)
 + [Pong-v0](https://gym.openai.com/evaluations/eval_8L7SV59nSW6GGbbP3N4G6w)
++ [Phoenix-v0](https://gym.openai.com/evaluations/eval_uzUruiB3RRKUMvJIxvEzYA)
 + [Qbert-v0](https://gym.openai.com/evaluations/eval_wekCJkrWQm9NrOUzltXg)
++ [Riverraid-v0](https://gym.openai.com/evaluations/eval_OU4x3DkTfm4uaXy6CIaXg)
++ [RoadRunner-v0](https://gym.openai.com/evaluations/eval_wINKQTwxT9ipydHOXBhg)
++ [Robotank-v0](https://gym.openai.com/evaluations/eval_Gr5c0ld3QACLDPQrGdzbiw)
 + [Seaquest-v0](https://gym.openai.com/evaluations/eval_N2624y3NSJWrOgoMSpOi4w)
 + [Tennis-v0](https://gym.openai.com/evaluations/eval_gDjJD0MMS1yLm1T0hdqI4g)
 + [UpNDown-v0](https://gym.openai.com/evaluations/eval_KmkvMJkxQFSED20wFUMdIA)
 + [VideoPinball-v0](https://gym.openai.com/evaluations/eval_PWwzNhVFR2CxjYvEsPfT1g)
++ [WizardOfWor-v0](https://gym.openai.com/evaluations/eval_1oGQhphpQhmzEMIYRrrp0A)
++ [Zaxxon-v0](https://gym.openai.com/evaluations/eval_TIQ102EwTrHrOyve2RGfg)
 
 Note that atari game settings in gym are quite different from DeepMind papers, so the scores are not comparable. The most notable differences are:
 + In gym, each action is randomly repeated 2~4 times.