PPO LunarLander-v2 after hpo

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 165.44 +/- 79.05
+      value: 119.96 +/- 120.32
       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 0x7fa995a5a280>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa995a5a310>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa995a5a3a0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa995a5a430>", "_build": "<function ActorCriticPolicy._build at 0x7fa995a5a4c0>", "forward": "<function ActorCriticPolicy.forward at 0x7fa995a5a550>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa995a5a5e0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa995a5a670>", "_predict": "<function ActorCriticPolicy._predict at 0x7fa995a5a700>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa995a5a790>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa995a5a820>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa995a5a8b0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fa995ad1bd0>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu", "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": 1001472, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1679385416731872502, "learning_rate": 0.0005022723199591951, "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": -0.0014719999999999178, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 11736, "n_steps": 128, "gamma": 0.988045661427605, "gae_lambda": 0.9768700030979546, "ent_coef": 0.004851331424615018, "vf_coef": 0.9683727099048757, "max_grad_norm": 0.5143831585332781, "batch_size": 128, "n_epochs": 24, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": {":type:": "<class 'function'>", ":serialized:": "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"}, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.15.0-67-generic-x86_64-with-glibc2.29 # 74~20.04.1-Ubuntu SMP Wed Feb 22 14:52:34 UTC 2023", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "2.0.0+cu117", "GPU Enabled": "True", "Numpy": "1.24.2", "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 0x7fc080fa54c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc080fa5550>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc080fa55e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc080fa5670>", "_build": "<function ActorCriticPolicy._build at 0x7fc080fa5700>", "forward": "<function ActorCriticPolicy.forward at 0x7fc080fa5790>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fc080fa5820>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc080fa58b0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fc080fa5940>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc080fa59d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc080fa5a60>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc080fa5af0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fc080fa2240>"}, "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": 500080, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1679389289642222321, "learning_rate": 0.0005022723199591951, "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": -0.00015999999999993797, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 107160, "n_steps": 7, "gamma": 0.988045661427605, "gae_lambda": 0.9768700030979546, "ent_coef": 0.004851331424615018, "vf_coef": 0.9683727099048757, "max_grad_norm": 0.5143831585332781, "batch_size": 7, "n_epochs": 24, "clip_range": {":type:": "<class 'function'>", ":serialized:": "gAWVJQMAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMeS9ob21lL3hhdmkvRG9jdW1lbnRzL3Byb2plY3RzL0RlZXBSTC1odWdnaW5nZmFjZS9kZWVwcmwtZW52L2xpYi9weXRob24zLjgvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuCQwIAAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjHkvaG9tZS94YXZpL0RvY3VtZW50cy9wcm9qZWN0cy9EZWVwUkwtaHVnZ2luZ2ZhY2UvZGVlcHJsLWVudi9saWIvcHl0aG9uMy44L3NpdGUtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/bPUA6d5h4hZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "clip_range_vf": {":type:": "<class 'function'>", ":serialized:": "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"}, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.15.0-67-generic-x86_64-with-glibc2.29 # 74~20.04.1-Ubuntu SMP Wed Feb 22 14:52:34 UTC 2023", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "2.0.0+cu117", "GPU Enabled": "True", "Numpy": "1.24.2", "Gym": "0.21.0"}}

ppo_lunarlander-v2_hpo.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:efd77a6600cb63ba60c03e7fa2a187703ad6b53c41eacc247cf41d2c12651d50
+size 148864

ppo_lunarlander-v2_hpo/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

ppo_lunarlander-v2_hpo/data

@@ -0,0 +1,98 @@
+{
+    "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 0x7fc080fa54c0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc080fa5550>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc080fa55e0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc080fa5670>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fc080fa5700>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fc080fa5790>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fc080fa5820>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc080fa58b0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fc080fa5940>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc080fa59d0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc080fa5a60>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc080fa5af0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fc080fa2240>"
+    },
+    "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": 500080,
+    "_total_timesteps": 500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1679389289642222321,
+    "learning_rate": 0.0005022723199591951,
+    "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": -0.00015999999999993797,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 107160,
+    "n_steps": 7,
+    "gamma": 0.988045661427605,
+    "gae_lambda": 0.9768700030979546,
+    "ent_coef": 0.004851331424615018,
+    "vf_coef": 0.9683727099048757,
+    "max_grad_norm": 0.5143831585332781,
+    "batch_size": 7,
+    "n_epochs": 24,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "normalize_advantage": true,
+    "target_kl": null
+}

ppo_lunarlander-v2_hpo/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5e9c033ff4db4b4c5d4635215bf72989d7f283c10b9ec9787e50b8a02058b626
+size 87929

ppo_lunarlander-v2_hpo/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c5bcf2715d44705fe7d110bffa428469603c527bc88a3b0238f510424ae535f2
+size 43393

ppo_lunarlander-v2_hpo/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

ppo_lunarlander-v2_hpo/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.15.0-67-generic-x86_64-with-glibc2.29 # 74~20.04.1-Ubuntu SMP Wed Feb 22 14:52:34 UTC 2023
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 2.0.0+cu117
+- GPU Enabled: True
+- Numpy: 1.24.2
+- Gym: 0.21.0

results.json

@@ -1 +1 @@
-{"mean_reward": 165.44318946995892, "std_reward": 79.04957207284303, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-21T09:52:06.749544"}
+{"mean_reward": 119.96047343111745, "std_reward": 120.32348713766541, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-21T11:57:57.841700"}