Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 259.17 +/- 18.54
+      value: 247.16 +/- 14.63
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +1 @@
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It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7f55a9350ee0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f55a9350f70>", "reset_noise": 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+        ":serialized:": "gAWVPgwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQGve8kleF+OMAWyUTUwBjAF0lEdApuRVwgkkbHV9lChoBkdAcRbKWszVMGgHTT8BaAhHQKbk64CIUJx1fZQoaAZHQGze+Lehwl1oB008AWgIR0Cm5t+Ad4mkdX2UKGgGR0BkWdkpZwGXaAdN6ANoCEdApub8pTdcjnV9lChoBkdAcZUtOEdvKmgHTVoBaAhHQKbnI8ujASF1fZQoaAZHQG98T6rNnoRoB002AWgIR0Cm5zrqD9OzdX2UKGgGR0BvlEILPUrkaAdNVAFoCEdApumuAf+0gXV9lChoBkdAbhau9OARTWgHTSMBaAhHQKbqa69TP0J1fZQoaAZHQDLwp5NXYDloB0vOaAhHQKbrI0IC2c91fZQoaAZHQHAa6IJqqOtoB00lAWgIR0Cm7CZkkKNRdX2UKGgGR0Bw5m5rgwXZaAdNPAFoCEdApu7Q7gbZOHV9lChoBkdAbZbxT850bWgHTT0BaAhHQKb/S/wiJO51fZQoaAZHQHBAiDM/yG1oB02yAWgIR0CnATS4vvjPdX2UKGgGR0A6EYZEUj9oaAdL52gIR0CnAlS5iExqdX2UKGgGR0Bw0EfvF3pwaAdNMgFoCEdApwPt7tzCDXV9lChoBkdAcWDT0QK8c2gHTQMBaAhHQKcEKUQCjlB1fZQoaAZHQG/OL+xW1dBoB01BAWgIR0CnB2T1K5CodX2UKGgGR0ByUewIMSbpaAdNRgFoCEdApwf1Cb+cY3V9lChoBkdAbLM0EX+ERWgHTTEBaAhHQKcJZtMwlB11fZQoaAZHQG5325paibloB00jAWgIR0CnCYNk4FRpdX2UKGgGR0BvW/HHWBjGaAdNIAFoCEdApwoypm29c3V9lChoBkdAbv98CxNZeWgHTVABaAhHQKcN9lJ6IFh1fZQoaAZHQGSWSGzru6VoB03oA2gIR0CnDspoTPB0dX2UKGgGR0Bt04c/+sHTaAdNNQFoCEdApw9hAMUh3nV9lChoBkdAcmH5/LDAJ2gHTTsBaAhHQKcQTzTWoWJ1fZQoaAZHQHGtPaQFLWZoB001AWgIR0CnEfeoLofTdX2UKGgGR0BvWd8gIQe4aAdNLgFoCEdApxRpm7J4jnV9lChoBkdAcPNVopQUH2gHTeUDaAhHQKcUdN8ma6V1fZQoaAZHQG5gQPI4lyBoB00OAWgIR0CnFlcJMQEqdX2UKGgGR0BxHbXz19ORaAdNJgFoCEdApxkvFm4Aj3V9lChoBkdAbRvl2/zreWgHTSoBaAhHQKcZiwAU+LZ1fZQoaAZHQHEJOlwcYIloB01bAWgIR0CnGjNZFG5MdX2UKGgGR0BRbMHB1s+FaAdL62gIR0CnHBJrtVrAdX2UKGgGR0A+zBBiTdLyaAdNBgFoCEdApxw0HY6GQHV9lChoBkdATPu0kWykbmgHS+BoCEdApxx8qJ/G2nV9lChoBkdAXiLtLL6k7GgHTegDaAhHQKcf8F4cFQl1fZQoaAZHQHIq62OQyRBoB008AWgIR0CnIACYLLIQdX2UKGgGR0BI8TewcHW0aAdNBgFoCEdApyBxQUHpr3V9lChoBkdAb3gFJxvNvGgHTRkBaAhHQKckApiI+GJ1fZQoaAZHQHG/jgIhQnBoB018AWgIR0CnKn2912aEdX2UKGgGR0BwSEsoUi6haAdNNAFoCEdApyuLZJ04i3V9lChoBkdAWSQTsY2sJmgHTegDaAhHQKcr3I5HVgB1fZQoaAZHQHACdAs052hoB003AWgIR0CnLB+V1Oj7dX2UKGgGR0Bwtc7PppvhaAdNMAFoCEdApyxm/5+H8HV9lChoBkdAckFv/io86mgHTRABaAhHQKcsk1VHWjJ1fZQoaAZHQG/+O8TSLIhoB00zAWgIR0CnLk2+oLofdX2UKGgGR0Bw0rUoa1kUaAdNCQFoCEdApy/VRNyo43V9lChoBkdAcFT79AHE/GgHTakCaAhHQKcwtsWweNl1fZQoaAZHQG9+E/0NBnloB00oAWgIR0CnMY6nrIHUdX2UKGgGR0BuqHWMCLdfaAdNaAFoCEdApzG2b5M10nV9lChoBkdAcHGY5DJEIGgHTUcBaAhHQKcznq+rU9Z1fZQoaAZHQHHkNjwx33ZoB01ZAWgIR0CnS3WxY7q6dX2UKGgGR0ByaXsAvL5iaAdL/WgIR0CnTXb6guh9dX2UKGgGR0BhBCzJIUaiaAdN6ANoCEdAp04PWlMyrXV9lChoBkdAbt374SHuZ2gHTQ4BaAhHQKdPFnnMdLh1fZQoaAZHQG8q3Mpw0fpoB00fAWgIR0CnT2n5SFXadX2UKGgGR0BT7gxJul41aAdN6ANoCEdAp0/lHJ9y93V9lChoBkdAb4CcDr7fpGgHTUsBaAhHQKdQzPVurIZ1fZQoaAZHQG/wHBk7OmloB00PAWgIR0CnUO/ICEHudX2UKGgGR0Bwpjf+CK77aAdNSAFoCEdAp1FE8/2TPnV9lChoBkdAcS+snRb8nGgHTT8BaAhHQKdRd5yEL6V1fZQoaAZHQHIHetr9EThoB01TAWgIR0CnVXNIK+i8dX2UKGgGR0ByadfBvaUSaAdNOQFoCEdAp1Wp6hQFcXV9lChoBkdAcYLl+EytWGgHTUUBaAhHQKdWXYywfQt1fZQoaAZHQHIRNoBaLXNoB00yAWgIR0CnVzlYMfA9dX2UKGgGR0BxJAl0HQhPaAdNjAFoCEdAp1jj2YfGMnV9lChoBkdAcPTbyH2ys2gHTTABaAhHQKdbN2LYPG11fZQoaAZHQG+xeaBqbjNoB00gAWgIR0CnXG33Hq/udX2UKGgGR0Bx5rDiwSrYaAdNSAFoCEdAp14eXqqwQnV9lChoBkdAbjFouf29MGgHTS4BaAhHQKdewnrpqyp1fZQoaAZHQHDZ24y44IdoB00+AWgIR0CnXu2KVII4dX2UKGgGR0Btslyq+8GtaAdNLAFoCEdAp1+MpCrtFHV9lChoBkdAcCnbxVhkRWgHTdADaAhHQKdfm7fYSQJ1fZQoaAZHQG4zSTQmeDpoB00rAWgIR0CnX6W2G7BgdX2UKGgGR0BxSICEHt4SaAdNJwFoCEdAp1/D/Q0GeXV9lChoBkdAcDY7o0Q9R2gHTU0BaAhHQKdf1Npudf91fZQoaAZHQGzPw9ic5KhoB01GAWgIR0CnYQ89GI9DdX2UKGgGR0BwAXDgqEvkaAdNXAFoCEdAp2YoBq9GqnV9lChoBkdAcDyE0zj3mGgHTV0BaAhHQKdnRSUkfLd1fZQoaAZHQHA5R3iaRZFoB01dAWgIR0CnaoRkNFz/dX2UKGgGR0BJoitJWeYlaAdLwmgIR0Cna1/I0ZWJdX2UKGgGR0BuBT2OAAhjaAdNGAFoCEdAp2x1Mh5gPXV9lChoBkdAcQswg1WKdmgHTVQBaAhHQKdszKVY6n11fZQoaAZHQHEpHmaH9FZoB00TAWgIR0CnbNs5wOvudX2UKGgGR0BvX+mHgxagaAdNGAFoCEdAp24CJ9AoonV9lChoBkdAbdKVlf7aZmgHTVoBaAhHQKduWtnwob51fZQoaAZHQHIT4RywOe9oB00dAWgIR0CnbplDfFaTdX2UKGgGR0BxDrA44p+daAdNKwFoCEdAp28Byp71I3V9lChoBkdAcdXhybQTmGgHTUgBaAhHQKdvrTXJ5mh1fZQoaAZHQHGh63RXwLFoB00/AWgIR0Cnb9jR2KVIdX2UKGgGR0Bt6po/RmbtaAdNhQFoCEdAp3MHaYeDF3V9lChoBkdAcC+n/DLr5mgHTTsBaAhHQKd3xY5ksjF1fZQoaAZHQHHH6rq+rU9oB01VAWgIR0CneCPCVKPGdX2UKGgGR0BxHWOR1X/6aAdNogJoCEdAp3lmxD9fkXV9lChoBkdAcsoFNL127mgHTRQBaAhHQKd8FiaRZEF1fZQoaAZHQHC0Wg3974VoB002AWgIR0CnfL5iuuA7dX2UKGgGR0BxyYGs3hn8aAdNFgFoCEdAp3zYp+c6NnV9lChoBkdAcaPbYbsF+2gHTTcBaAhHQKd82g13t8h1fZQoaAZHQHCwAAIY3vRoB00CAWgIR0CnfQDurp7kdX2UKGgGR0BwlYZuQ6p6aAdNVgFoCEdAp3/G0PYnOXVlLg=="
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
@@ -54,27 +53,30 @@
     },
     "_n_updates": 248,
     "observation_space": {
-        ":type:": "<class 'gym.spaces.box.Box'>",
-        ":serialized:": "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",
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
         "dtype": "float32",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
         "_shape": [
             8
         ],
-        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
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 - Python: 3.9.16
-- Stable-Baselines3: 1.8.0
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 - PyTorch: 1.12.1+cu116
 - GPU Enabled: True
 - Numpy: 1.23.4
-- Gym: 0.21.0
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