test commit with 5000 iterations from unit1 notebook from local jupyterhub

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 274.20 +/- 19.59
+      value: -845.44 +/- 788.77
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +1 @@
-{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7f9c2f2e84c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9c2f2e8550>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9c2f2e85e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9c2f2e8670>", "_build": "<function ActorCriticPolicy._build at 0x7f9c2f2e8700>", "forward": "<function ActorCriticPolicy.forward at 0x7f9c2f2e8790>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f9c2f2e8820>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9c2f2e88b0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f9c2f2e8940>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9c2f2e89d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9c2f2e8a60>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9c2f2e8af0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f9c2f2edec0>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1679656003778989495, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 252, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7f34e9a90430>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f34e9a904c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f34e9a90550>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f34e9a905e0>", "_build": "<function ActorCriticPolicy._build at 0x7f34e9a90670>", "forward": "<function ActorCriticPolicy.forward at 0x7f34e9a90700>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f34e9a90790>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f34e9a90820>", "_predict": "<function ActorCriticPolicy._predict at 0x7f34e9a908b0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f34e9a90940>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f34e9a909d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f34e9a90a60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f34e9a91900>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 32768, "_total_timesteps": 5000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1679900547316176517, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAEAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -5.5536, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 8, "n_steps": 2048, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 8, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.14.0-239.el9.x86_64-x86_64-with-glibc2.34 # 1 SMP PREEMPT_DYNAMIC Thu Jan 19 14:14:19 UTC 2023", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu117", "GPU Enabled": "True", "Numpy": "1.24.1", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b8f52cd34f8416ccbff4694ba0d80295eb4c950b6f461c5e08c06003a957d1d8
-size 147393
+oid sha256:d87acc0ab23e11c172830d527382d34cee4afb39c4fcb9cd359b4bd322bd6e06
+size 147336

ppo-LunarLander-v2/data

@@ -4,20 +4,20 @@
         ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
         "__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 0x7f9c2f2e84c0>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9c2f2e8550>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9c2f2e85e0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9c2f2e8670>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f9c2f2e8700>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f9c2f2e8790>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f9c2f2e8820>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9c2f2e88b0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f9c2f2e8940>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9c2f2e89d0>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9c2f2e8a60>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9c2f2e8af0>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f34e9a90430>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f34e9a904c0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f34e9a90550>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f34e9a905e0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f34e9a90670>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f34e9a90700>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f34e9a90790>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f34e9a90820>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f34e9a908b0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f34e9a90940>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f34e9a909d0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f34e9a90a60>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7f9c2f2edec0>"
+        "_abc_impl": "<_abc._abc_data object at 0x7f34e9a91900>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -43,51 +43,51 @@
         "_np_random": null
     },
     "n_envs": 16,
-    "num_timesteps": 1015808,
-    "_total_timesteps": 1000000,
+    "num_timesteps": 32768,
+    "_total_timesteps": 5000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1679656003778989495,
+    "start_time": 1679900547316176517,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
         ":type:": "<class 'function'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
+        ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAEAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
     },
     "_last_original_obs": null,
     "_episode_num": 0,
     "use_sde": false,
     "sde_sample_freq": -1,
-    "_current_progress_remaining": -0.015808000000000044,
+    "_current_progress_remaining": -5.5536,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 252,
-    "n_steps": 1024,
+    "_n_updates": 8,
+    "n_steps": 2048,
     "gamma": 0.999,
     "gae_lambda": 0.98,
     "ent_coef": 0.01,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
     "batch_size": 64,
-    "n_epochs": 4,
+    "n_epochs": 8,
     "clip_range": {
         ":type:": "<class 'function'>",
-        ":serialized:": "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"
+        ":serialized:": "gAWV0QIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMTy9ob21lL21pa2kvLmxvY2FsL2xpYi9weXRob24zLjkvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuCQwIAAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjE8vaG9tZS9taWtpLy5sb2NhbC9saWIvcHl0aG9uMy45L3NpdGUtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
     },
     "clip_range_vf": null,
     "normalize_advantage": true,

ppo-LunarLander-v2/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c548a855a1b7e64a2b0671dab17dfff500256d899889ba202d4ac252105e0c18
+oid sha256:b70dcd962d6d334d15666fbe14d34a089709e511a770ce968f3728ef512ad77b
 size 87929

ppo-LunarLander-v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c23a0ec5dcf357463d8d438f666ad40d5a620795d7e990817d45d27b37f0f9a0
+oid sha256:3761eac176cd36f4b81f0cef1d8c0dd08ebfed949d5d0911e24ea6dae96a0d5a
 size 43393

ppo-LunarLander-v2/system_info.txt

@@ -1,7 +1,7 @@
-- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- OS: Linux-5.14.0-239.el9.x86_64-x86_64-with-glibc2.34 # 1 SMP PREEMPT_DYNAMIC Thu Jan 19 14:14:19 UTC 2023
 - Python: 3.9.16
 - Stable-Baselines3: 1.7.0
-- PyTorch: 1.13.1+cu116
+- PyTorch: 1.13.1+cu117
 - GPU Enabled: True
-- Numpy: 1.22.4
+- Numpy: 1.24.1
 - Gym: 0.21.0

results.json

@@ -1 +1 @@
-{"mean_reward": 274.2046822267237, "std_reward": 19.585962574946056, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-24T11:27:48.154092"}
+{"mean_reward": -845.4369092740119, "std_reward": 788.773603836604, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-27T09:11:08.898059"}