fourth submission

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 258.62 +/- 19.85
+      value: 272.33 +/- 25.12
       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 0x7f7b33e5b6d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7b33e5b760>", "reset_noise": 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         "__module__": "stable_baselines3.common.policies",
         "__doc__": "\n    Policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). 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 0x7f7b33e5b6d0>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7b33e5b760>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7b33e5b7f0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7b33e5b880>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f7b33e5b910>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f7b33e5b9a0>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f7b33e5ba30>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7b33e5bac0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f7b33e5bb50>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7b33e5bbe0>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7b33e5bc70>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7b33e5bd00>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x78afcbc51090>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78afcbc51120>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78afcbc511b0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78afcbc51240>",
+        "_build": "<function ActorCriticPolicy._build at 0x78afcbc512d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x78afcbc51360>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x78afcbc513f0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78afcbc51480>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x78afcbc51510>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78afcbc515a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78afcbc51630>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x78afcbc516c0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7f7b33e5dc00>"
+        "_abc_impl": "<_abc._abc_data object at 0x78afeaf5e740>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -26,12 +26,12 @@
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1709865576847869968,
+    "start_time": 1710123958252548466,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "_last_obs": {
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     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -45,7 +45,7 @@
     "_stats_window_size": 100,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
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+        ":serialized:": "gAWVBwwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQHDMxMnJDE6MAWyUS/6MAXSUR0CMs/TYNAkcdX2UKGgGR0BvTXsPatcOaAdL82gIR0CMtHDjR2KVdX2UKGgGR0BzA+MQ2/BWaAdLz2gIR0CMtIOXE61cdX2UKGgGR0Bwpsq8UVSGaAdNCAFoCEdAjLWMG5c1O3V9lChoBkdAc0AE2YOUdWgHTRABaAhHQIy2oQarFOx1fZQoaAZHQHCrmBjFyaNoB00eAWgIR0CMtztRekYXdX2UKGgGR0BshXLidat+aAdNBwFoCEdAjLgu1F6RhnV9lChoBkdAboNU8V58jWgHS9xoCEdAjLia8xsVL3V9lChoBkdAcac+ZgG8mWgHTY4BaAhHQIy40PSUkfN1fZQoaAZHQHAlg2qDK5loB0vkaAhHQIy55Nj9XLh1fZQoaAZHQHLeC/sVtXRoB00JAWgIR0CMukiJwbVCdX2UKGgGR0By+T5xiobXaAdNGgFoCEdAjLsnJtBOYnV9lChoBkdAbqpuQ6p5vGgHS+poCEdAjLs+dkJ8fHV9lChoBkdAcfLpmmLtNWgHTSwBaAhHQIy73ZAY51h1fZQoaAZHQHFpIgeRxLloB0vqaAhHQIy/koYvWYp1fZQoaAZHQHJ5OR5kbxVoB0vnaAhHQIzAJ19v0iB1fZQoaAZHQHIEv7SApa1oB0vXaAhHQIzAXnU2DQJ1fZQoaAZHQHGdJtNzr/toB0v3aAhHQIzAkWCVbA11fZQoaAZHQG8D02tMfzVoB0v4aAhHQIzBE2BJ7LN1fZQoaAZHQHGL6VdHDrJoB0viaAhHQIzDemHgxah1fZQoaAZHQHCZjbN8ma9oB0v9aAhHQIzEDFhoduJ1fZQoaAZHQHGKDyvs7dVoB0viaAhHQIzELhWHUMJ1fZQoaAZHQHJQCnLq2SdoB01MAWgIR0CMxSGmk30gdX2UKGgGR0BxxeN0eU6gaAdNPQFoCEdAjMb9TP0I1XV9lChoBkdAcS2tuDSPVGgHTQUBaAhHQIzHtqesgdR1fZQoaAZHQG7G/KyOaORoB0v2aAhHQIzH+z0HyEt1fZQoaAZHQHLKDuBtk4FoB0v5aAhHQIzIDAUL2Ht1fZQoaAZHQG9NzY/Vy3loB01GAWgIR0CMyV+irT6SdX2UKGgGR0ByOy6XjU/faAdNGgFoCEdAjMqNWU8mr3V9lChoBkdAbjOZNO/L1WgHTV4BaAhHQIzL/ARChOB1fZQoaAZHQG/w+LFXJYFoB0viaAhHQIzMU4ku6Et1fZQoaAZHQHETRoduHetoB0v6aAhHQIzN8n/kvK51fZQoaAZHQG/Eg/1QIldoB00HAWgIR0CMzmwIMSbpdX2UKGgGR0BzukDyOJcgaAdL/WgIR0CM8A8BdUsGdX2UKGgGR0BzKajtXxOMaAdNJQFoCEdAjPC/apPykXV9lChoBkdAcYE42S+xnmgHS+FoCEdAjPEPBacI7nV9lChoBkdAcL8l/YraumgHS+NoCEdAjPHaJAMUh3V9lChoBkdAb1QsAeaKDWgHS+BoCEdAjPHRhMJyAHV9lChoBkdAcMqBo24usmgHS/doCEdAjPRlKbrkbXV9lChoBkdAbtfDohY/3WgHS/BoCEdAjPY8SGrS3XV9lChoBkdAbdCgntv4umgHS+FoCEdAjPZx+BpYcXV9lChoBkdAcnHcu8K5TmgHS+RoCEdAjPa3aSLZSXV9lChoBkdAcgWDuSfUWmgHS+xoCEdAjPbbTUiIL3V9lChoBkdAUGIsasIVumgHS9ZoCEdAjPseVLSNO3V9lChoBkdAckKFirksBmgHTQ4BaAhHQIz7HEjxCpp1fZQoaAZHQHFJU87p3X9oB0v2aAhHQIz9DSZ0CBB1fZQoaAZHQHGZ+rQw9JVoB00hAWgIR0CM/kWQfZEldX2UKGgGR0Bx6FdPci4baAdLzGgIR0CM//A4XGfgdX2UKGgGR0Byp/hn8KoiaAdL7WgIR0CNAH69CeEqdX2UKGgGR0BxGq3azu4PaAdL2GgIR0CNANKdxyXEdX2UKGgGR0BxT9US7GvPaAdL/mgIR0CNATefI0ZWdX2UKGgGR0Bw8GPbO/tZaAdL9GgIR0CNAXyp71IzdX2UKGgGR0Byjsxgy/KyaAdNGAFoCEdAjQH+t0V8C3V9lChoBkdAcbgeaKDTSmgHTQ0BaAhHQI0CxzLfUF11fZQoaAZHQHLBU74i5d5oB00HAWgIR0CNBRnwob4rdX2UKGgGR0BzqOZSeiBYaAdL8GgIR0CNBbUwztTldX2UKGgGR0BxdLSPU8V6aAdNAQFoCEdAjQZldszl93V9lChoBkdAcVg+aScLB2gHTQUBaAhHQI0G7fcer+51fZQoaAZHQG60oQWepXJoB0vfaAhHQI0ISe2/i5x1fZQoaAZHQHEknPiT+vRoB0vjaAhHQI0IjDZUT+N1fZQoaAZHQHCr3JkoWpJoB001AWgIR0CNCNNnGsFMdX2UKGgGR0Bx25NO/L1VaAdL6GgIR0CNCfEa2nbZdX2UKGgGR0Bw+5nUUfxMaAdL42gIR0CNDET+vQnhdX2UKGgGR0Bzezh99c8laAdL8GgIR0CNDGAFPi1idX2UKGgGR0By8o/0NBnjaAdL6mgIR0CNDGPxx1gZdX2UKGgGR0BySFKVY6n0aAdL5WgIR0CNDQ4BmwqzdX2UKGgGR0BwGhLCemNzaAdNIgFoCEdAjQ3gEt/WlXV9lChoBkdAb/kVkc0cfmgHS/BoCEdAjQ4owEhaDHV9lChoBkdAUo9Y9xIatWgHS6BoCEdAjQ8Q/PgNw3V9lChoBkdAbqf9roGIK2gHTRgBaAhHQI0PZnBciW51fZQoaAZHQG8bfoRqXWxoB00RAWgIR0CNEMQyyleodX2UKGgGR0ByxQWfseGPaAdL2WgIR0CNEWXizcASdX2UKGgGR0BxdqJYT0xuaAdNAgFoCEdAjRKzRYzSC3V9lChoBkdAcmhVnmJWNmgHTRwBaAhHQI0TakIomXx1fZQoaAZHQG+xYgzP8htoB0v5aAhHQI0VCxeLNwB1fZQoaAZHQHFkPu5SWJJoB00AAWgIR0CNFbrftQbddX2UKGgGR0BxjOVbA1vVaAdLyWgIR0CNFnDTBqKxdX2UKGgGR0ByQp5fMOf/aAdNHAFoCEdAjRaXTVlPJ3V9lChoBkdAcuFyWzF+/mgHS+9oCEdAjRhKKgqVhXV9lChoBkdAcmn642CNCWgHTR0BaAhHQI0YYLkS26V1fZQoaAZHQHBxPTG5tnBoB0v3aAhHQI0ZWDnNgSh1fZQoaAZHQHNxwXZXdTJoB00OAWgIR0CNGbJPqLTAdX2UKGgGR0ByNeIwdsBRaAdL8GgIR0CNGg/+KjzqdX2UKGgGR0BzC5h6Skj5aAdL+GgIR0CNGjA8jiXIdX2UKGgGR0Byq+rlvIfbaAdL3GgIR0CNGjqqOtGNdX2UKGgGR0BOqGYBvJiiaAdLsmgIR0CNHApRXOnmdX2UKGgGR0ByzwG+sYEXaAdNGAFoCEdAjRyuxrzoU3V9lChoBkdAcZ2b1RLsbGgHTQwBaAhHQI0dvRNRFZx1fZQoaAZHQHFki5NGmUJoB00VAWgIR0CNHtkH2RJVdX2UKGgGR0BztKyeI2wWaAdNCQFoCEdAjR+gElme2HV9lChoBkdAcDdIXTEzf2gHS/FoCEdAjSDTySV4YHV9lChoBkdAcqsLVFx4p2gHS/FoCEdAjSGT544ZM3V9lChoBkdAclWmXPZ7HGgHS+toCEdAjSRVGsmv4nV9lChoBkdAce5IcinpCGgHS9RoCEdAjSVOXeFcp3V9lChoBkdAb2LZCfHxSmgHS/1oCEdAjSVihew9q3V9lChoBkdAcyvthd+ocmgHS+xoCEdAjSYQpON5t3V9lChoBkdAcJeWKuSwGGgHS+ZoCEdAjSZFenhsInV9lChoBkdAcFEcDbJwKmgHS/poCEdAjSZo73fygHV9lChoBkdAce+1He7+UGgHTTgBaAhHQI0mikXUH6d1fZQoaAZHQG+DfNJOFg5oB00CAWgIR0CNJ4BYFJQMdWUu"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
@@ -87,13 +87,13 @@
     "n_epochs": 4,
     "clip_range": {
         ":type:": "<class 'function'>",
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     },
     "clip_range_vf": null,
     "normalize_advantage": true,
     "target_kl": null,
     "lr_schedule": {
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 size 88362

ppo-LunarLander-v2/policy.pth

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 size 43762

ppo-LunarLander-v2/system_info.txt

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-- OS: Linux-5.15.146.1-microsoft-standard-WSL2-x86_64-with-glibc2.35 # 1 SMP Thu Jan 11 04:09:03 UTC 2024
+- OS: Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
 - Python: 3.10.12
 - Stable-Baselines3: 2.0.0a5
-- PyTorch: 2.2.1+cu121
+- PyTorch: 2.1.0+cu121
 - GPU Enabled: True
-- Numpy: 1.26.4
-- Cloudpickle: 3.0.0
+- Numpy: 1.25.2
+- Cloudpickle: 2.2.1
 - Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

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